Polymorphic functionalization driven by ion displacement-induced antiferroelectric ordering in CuBiP₂Se₆

Domain: physics
Published: 2025-11-27
Editorial outcome: major_revision

Abstract
Antiferroelectric two-dimensional materials, with their unique physical mechanisms, exhibit tunable polarization dynamics and layered structural characteristics, enabling the synergistic implementation of synaptic plasticity, sensory-mimetic functionality, and in-memory computing within a unified device architecture. These capabilities meet the growing polymorphic requirements of neuromorphic systems and position such materials as strong candidates for next-generation neuromorphic computing platforms. Among them, CuBiP₂Se₆ stands out among 2D antiferroelectric materials due to its intrinsic antiferroelectric properties, featuring a stable interlayer antiparallel Cu⁺ dipole configuration. This structure, combined with its relaxor-like behavior, enables a reversible transition between antiferroelectric and ferroelectric states under an applied electric field, along with gradual polarization tuning. This transition mechanism enables continuously tunable conductance states, providing essential physical support for the gradual modulation of synaptic weights and the hardware implementation of complex neural functions, making it particularly suited for high-precision emulation of multilevel synaptic plasticity in neuromorphic applications. In this work, memristor based on two-dimensional antiferroelectric CuBiP₂Se₆ exhibit stable multilevel conductance states, high endurance, and excellent device uniformity, thus supporting diverse neurosynaptic functions and advanced learning rules. These attributes highlight the immense potential of antiferroelectric 2D materials as a foundation for compact, energy-efficient, and highly integrated neuromorphic hardware.

Introduction
The surging demand for energy-efficient, multifunctional computing power has revealed the limitations of traditional von Neumann architectures, notably serial processing and escalating power dissipation1–6. As a result, neuromorphic computing, which is modeled on the parallel and low-information processing of the brain, has come to the forefront7,8. Next-generation neuromorphic systems require hardware capable of emulating complex biological learning rules and supporting a broad spectrum of synaptic functions9–14. While memristors have advanced in-memory and compute-in-memory architectures by leveraging their analog and nonvolatile resistive switching, most current devices- as illustrated in Fig. 1a are limited to first-order synaptic dynamics, such as short-term plasticity (STP) and basic spike-timing-dependent plasticity (STDP)15–18. Although STDP circuits excel at certain tasks, their reinforcement-based nature restricts synaptic stability and adaptability, making them inadequate for capturing the richer temporal and rate-based synaptic modulation observed in biological systems19.

To address these challenges, materials and device platforms must go beyond simple Hebbian learning, enabling history-dependent, nonlinear, and homeostatic plasticity like BCM (Bienenstock-Cooper-Munro) learning20–33. Among them, the BCM rule17,34 represents a rate-based synaptic plasticity model that extends the classical Hebbian paradigm by introducing the average postsynaptic firing rate as a regulatory factor, thereby enabling synaptic weight updates to reflect the historical activity of the neuron (Fig. 1b). Theoretically, the time-dependent dynamic processes inherent in physical mechanisms such as ion migration and trap-state modulation offer promising avenues for implementing history-dependent and nonlinearly evolving weight updates required by the BCM rule24–26,30. However, the practical realization of BCM-compatible behavior and the required multifunctional synaptic modulation at the device level still faces considerable challenges, such as unstable conductive pathways, poor performance uniformity, limited reproducibility, and insufficient endurance. These issues are primarily attributed to the immaturity of material systems, the high complexity of multifunctional integration, and the challenges associated with device fabrication processes.

Compared with conventional ferroelectric (FE) systems, antiferroelectric (AFE) materials—with their near-zero remanent polarization and antiparallel dipole configurations—can achieve analog, continuously tunable conductance modulation, making them ideal candidates for energy-efficient neuromorphic devices35–39. Their unique polarization-switching mechanism further endows them with higher breakdown fields36,40, faster polarization switching speeds41,42, lower energy dissipation36,43, and excellent fatigue resistance44,45. In particular, relaxor-type AFE materials exhibit gradual polarization responses under external electric fields and slowly relax back once the field is removed46,47. Such dynamical characteristics more closely resemble the time-dependent and history-sensitive behaviors of biological synapses, rendering them especially suitable for hardware implementation of sliding-threshold regulation in BCM-type plasticity.

To the best of our knowledge, the van der Waals layered compound CuBiP₂Se₆ (CBPS) is the first 2D AFE semiconductor and have outstanding properties: it possesses intrinsic antiferroelectric order with antiparallel dipoles, enabling nonlinear, history-dependent, and analog conductance modulation. Furthermore, its dangling-bond-free surfaces and atomically thin geometry ensure excellent interfacial quality, scalability, and compatibility with silicon-based CMOS technologies48–52, distinguishing CBPS as a promising platform for mimicking complex synaptic functionalities in neuromorphic applications.

In this work, we report a vertical memristor array based on 2D relaxor AFE CBPS. The devices exhibit robust multilevel conductance modulation, stable bipolar switching (sustaining over 1000 cycles), and an overall yield exceeding 90%, reflecting high process reproducibility. Neuromorphic functionalities include paired-pulse facilitation, long-term potentiation/depression, and, through triplet-STDP pulse protocols, device-level manifestation of frequency-dependent weight updates and a sliding plasticity threshold effectively mimics key aspects of BCM learning, enabling advanced forms of synaptic plasticity crucial for adaptive information processing. In addition, the devices demonstrate electrically driven nociceptor-like responses, responding sharply to suprathreshold inputs; this is analogous to how biological systems prioritize the rapid transmission of critical or potentially harmful sensory information. The integration of both BCM-type learning and nociceptive response in a single device reflects the polymorphic nature required for robust neuromorphic computing. Leveraging these dynamic synaptic features, device characteristics were mapped into an artificial neural network for facial recognition. The advanced synaptic plasticity empowered by BCM learning, together with the adaptive sensitivity derived from nociceptor-like response, jointly contribute to more effective feature extraction and information discrimination within the neural network. As a result, the system achieves a classification accuracy exceeding 90%, demonstrating not only excellent trainability but also the practical value of integrating diverse synaptic behaviors within neuromorphic hardware for next-generation artificial intelligence applications. This work establishes antiferroelectric 2D materials—specifically CBPS—as a powerful platform for polymorphic neuromorphic devices, combining fine-tuned and reliable synaptic emulation with CMOS compatibility and integration potential. These advances mark a significant step toward energy-efficient, highly adaptive brain-inspired hardware that can tackle the increasingly complex demands of future computing systems.

Results
The crystal structure of CBPS is illustrated in Fig. 1c (top view) and Fig. 1e (side view). To verify the chemical composition and structural integrity of the synthesized CBPS single crystals, comprehensive characterizations were conducted, as shown in Supplementary Fig. 1 Energy-dispersive X-ray spectroscopy (EDS) revealed a molar ratio of Cu, Bi, P, and Se close to 1:1:2:6, in good agreement with the theoretical stoichiometry of CBPS, confirming successful synthesis of the target compound. Elemental mapping further demonstrated uniform spatial distribution of the constituent elements across the nanosheet, without apparent aggregation or depletion, indicating excellent compositional homogeneity. High-resolution transmission electron microscopy (HRTEM) images, as shown in Fig. 1d, reveal well-aligned lattice fringes with no observable structural defects, indicating high crystal quality and a layered structure. In addition, the corresponding selected area electron diffraction (SAED) pattern displays sharp and well-defined diffraction spots with hexagonal symmetry, further confirming the single-crystalline nature of the CBPS flakes (Supplementary Fig. 2). In addition, the X-ray diffraction (XRD) pattern displayed in Supplementary Fig. 2 shows sharp and well-defined diffraction peaks that match well with the standard CBPS pattern, further confirming the phase purity and structural consistency of the material. The Raman spectrum of CBPS, as shown in Supplementary Fig. 3, exhibits several characteristic peaks at 111.7, 135.3, 207.5, 431.9, and 465.9 cm⁻¹. According to previous reports47,53–55, the low-frequency peaks at 111.7 and 135.3 cm⁻¹ are associated with lattice vibrations involving Bi-Se and Cu-Se bonds, while the dominant peak at 207.5 cm⁻¹ is attributed to the symmetric stretching vibration of the [P₂Se₆]⁴⁻ unit. The higher-frequency peaks at 431.9 and 465.9 cm⁻¹ correspond to the P-Se bond stretching vibrations within the [P₂Se₆]⁴⁻ group. Based on symmetry considerations, the peaks labeled in red represent out-of-plane modes, whereas those in blue correspond to in-plane modes. At room temperature, CBPS exhibits distinct AFE behavior. First-principles calculations by Reimers et al.46,47 suggest that CBPS consists of layered [P₂Se₆]⁴⁻ anionic frameworks interleaved with Cu⁺ and Bi³⁺ cations. Cu⁺ ions typically occupy octahedral sites, but due to their small ionic radius and weak coordination bonding, they tend to shift either vertically or laterally53 (Supplementary Fig. 4). These displacements modulate the local coordination environment, thereby tuning the polarization state and enabling electric-field-induced transitions between FE and AFE phases. When Cu⁺ ions displace vertically, local dipoles are formed. If adjacent layers exhibit opposite displacement directions, the resulting interlayer dipoles cancel out, leading to near-zero net polarization—a hallmark of AFE order.

Under a sufficiently strong electric field, the Cu⁺ ions align in the same direction, generating net polarization and inducing a transition from AFE to FE state, as illustrated in the schematic in Fig. 1e. This transition is typically accompanied by pronounced polarization responses. The AFE nature of CBPS was further validated by polarization-electric field (P-E) hysteresis measurements. As shown in Supplementary Fig. 5, the optical microscopy image and atomic force microscopy (AFM) scan reveal a ~20 nm-thick CBPS flake uniformly bridging the crossbar electrodes, ensuring a stable interface for electrical characterization. As shown in Fig. 1f, the device exhibits a characteristic double-loop P-E profile under an applied electric field. Unlike classical AFE materials that undergo abrupt polarization switching, CBPS shows smooth and gradual P-E loops, indicating a relaxor response. This behavior is attributed to the progressive alignment of initially disordered polar nanoregions under the electric field, a signature of relaxor AFE materials56,57. Furthermore, typical relaxor antiferroelectric features were consistently observed across a wide temperature range (210−330 K) and in devices with different thicknesses, as shown in Supplementary Fig. 6-7. This process was further corroborated by piezoresponse force microscopy (PFM) analysis. As shown in Fig. 1g, the PFM phase signal becomes increasingly pronounced upon continuous electrical stimulation, indicating electric-field-induced domain reorientation. In parallel, the corresponding PFM amplitude mapping shows a significant enhancement in signal intensity, reflecting a progressive increase in local piezoelectric response. These observations are consistent with the accumulation of aligned dipoles and confirm the presence of electric-field-driven domain evolution in CBPS. The characterization results confirm that the synthesized CBPS crystals exhibit a well-ordered layered structure, high crystallinity, and uniform composition. Their relaxor AFE behavior with smooth, tunable polarization and reversible transitions provides a reliable basis for CBPS-based memristor design and neuromorphic applications.

A vertical 5 × 5 memristor array was fabricated using the two-dimensional relaxor antiferroelectric material CBPS as the active layer, as schematically illustrated in Fig. 2a, with the optical image shown in Supplementary Fig. 8. Electrical measurements reveal that the device exhibits clear bipolar resistive switching under ±1.5 V voltage sweeps (Fig. 2b), which can be attributed to field-driven vertical Cu⁺ ion displacement and the associated polarization modulation. A compliance current of 100 μA was applied during testing to avoid over-current damage, and the high-resistance state (HRS) and low-resistance state (LRS) values were consistently extracted at a reading voltage of 0.5 V. Under these conditions, the device maintains a high ON/OFF conductance ratio (>10²) over 1000 consecutive switching cycles without performance degradation, demonstrating excellent endurance. The statistical distribution of HRS and LRS values over 1000 cycles, presented in Supplementary Fig. 9, together with the consecutive I–V switching characteristics of the CBPS-based memristor that remain stable even after 100 days (Supplementary Fig. 10) further confirms the stability and reproducibility of the resistive switching behavior. These results highlight that the layered crystal structure and antipolar configuration of CBPS enable robust and reversible switching characteristics. Additionally, the fabricated 5 × 5 array exhibited a device yield of 92% (23 out of 25 devices), which successfully demonstrated stable neuromorphic behaviors, including nonlinear current–voltage characteristics and reliable multi-pulse conductance responses (Supplementary Fig. 11-12), confirming the device’s reliability and compatibility with large-scale integration. Short-term synaptic plasticity characteristics were evaluated by applying paired voltage pulses (3 V, 20 ms) with varying inter-pulse intervals (Δt = 20–500 ms). As depicted in Fig. 2c, the second excitatory postsynaptic current (A₂) consistently exceeds the first (A₁), and the PPF index (A₂/A₁ × 100%) decays exponentially with increasing interval, closely emulating neurotransmitter residual effects observed in biological synapses.

The synaptic weight modulation was further investigated by tuning the pulse amplitude, number, and width. When increasing the write voltage from 3 V to 8 V under otherwise identical conditions (20 pulses, 20 ms interval), the device exhibits a stepwise increase in conductance under a non-destructive read voltage of 0.5 V (Fig. 2d). The inset highlights a cumulative increase in current with stronger electric fields, indicating enhanced Cu⁺ displacement and dipole alignment. Under fixed pulse conditions (3 V, 20 ms), the conductance increases progressively with pulse number (10, 20, 50) and saturates at higher levels (Fig. 2e), revealing a characteristic accumulation effect. Pulse width also plays a significant role: extending the duration from 1 ms to 40 ms results in notably higher conductance (Supplementary Fig. 13), further supporting the field-driven ionic displacement and gradual domain ordering in CBPS. Long-term synaptic plasticity was demonstrated by applying 50 potentiation pulses (+2.5 V, 10 ms) followed by 50 depression pulses (−1.2 V, 10 ms). As shown in Fig. 2f, the device conductance increases during potentiation and decreases during depression, with each cycle yielding reproducible and reversible behavior over ten iterations. This bidirectional plasticity closely mimics the dynamic modulation of synaptic strength in biological systems, underscoring the viability of CBPS memristors in implementing both short-term and long-term synaptic functions for neuromorphic computing architectures. In addition, noise robustness was confirmed by time-series and power spectral density (PSD) analyses, which showed stationary I(t) traces and 1/f α (α≈1) spectra without random telegraph noise features (Supplementary Fig. 14). Importantly, these synaptic plasticity characteristics were consistently observed across devices from different fabrication batches (Supplementary Figs. 15–17), confirming the reproducibility and robustness of the CBPS.

Voltage-dependent characteristics of long-term synaptic plasticity in CBPS-based memristors were investigated through a designed voltage-modulated stimulation protocol. As shown in Fig. 3a, each modulation cycle consisted of 10 consecutive pulses with linearly increasing amplitudes (1.2 V to 2.6 V) to emulate long-term potentiation (LTP), followed by a sequence of decreasing amplitudes (2.6 V to 1.2 V) to induce long-term depression (LTD), under the testing conditions summarized in Supplementary Fig. 18. This cycle was repeated 10 times, achieving stable bidirectional synaptic weight modulation purely by tuning the pulse amplitude, without altering pulse width or number. In addition to voltage-driven long-term plasticity, CBPS devices also emulate STDP, a fundamental rule governing timing-sensitive learning in biological synapses. Biologically, STDP modifies synaptic weight based on the time interval (Δt) between pre- and post-synaptic spikes: LTP occurs when the pre-spike precedes the post-spike (Δt > 0), while LTD is induced when the order is reversed (Δt < 0). A paired-pulse scheme was implemented using a single electrode, with a +3 V, 20 ms pulse representing the pre-spike and a −3 V, 20 ms pulse for the post-spike. Δt was varied from –200 ms to +200 ms. As shown in Fig. 3b, the resulting ΔG-Δt curve exhibits an asymmetric STDP window: potentiation in the positive Δt region and depression in the negative Δt region, consistent with biological behavior. To emulate triplet-STDP behavior observed in biological neurons, the conventional paired-pulse protocol was extended to a temporally controlled triplet-pulse scheme, as illustrated in Fig. 3c. Six pulse sequences composed of three voltage pulses (+3 V or –3 V, 20 ms each) were designed: Post-Pre-Pre, Pre-Post-Pre, Pre-Pre-Post, Pre-Post-Post, Post-Pre-Post, and Post-Post-Pre. Two-time intervals (Δt₁ and Δt₂) between adjacent spikes were defined, and the conductance change ΔG was mapped accordingly. Figure 3d, e display 2D heatmaps for representative patterns (Post-Pre-Post and Pre-Post-Pre), showing nonlinear dependence of synaptic weight on timing parameters, indicative of competitive and synergistic interactions between LTP and LTD processes. The mirrored symmetry between the two patterns highlights the system’s sensitivity to spike order. These behaviors originate from the internal polarization dynamics in CBPS: same-polarity pulses enhance Cu⁺ displacement and dipole alignment, while opposite or irregular sequences disturb the established polarization. BCM-type plasticity was further evaluated by applying identical stimulation parameters (+3 V, 20 ms) across varying pulse frequencies, starting from different initial conductance states (3.92, 7.0, and 16.1 nS). As shown in Fig. 3f, the ΔG-frequency curve exhibited a non-monotonic profile: depression at intermediate frequencies (~10-20 Hz) and potentiation at higher frequencies (>20 Hz). Importantly, the threshold frequency for the LTD-to-LTP transition decreased with increasing initial conductance, confirming a dynamic, activity-dependent modulation threshold. This sliding-threshold effect is in line with the core principle of the BCM rule, wherein synaptic modification thresholds adapt according to neuronal activity history. In addition, under fixed pulse parameters (+3 V, 20 ms; read voltage 0.5 V), the device’s response to different pulse frequencies was investigated (Fig. 3g). Four consecutive stimulation stages were applied sequentially: 25 Hz → 6.25 Hz → 1.47 Hz → 6.25 Hz. During the high-frequency stimulation (25 Hz), the device exhibited a significant conductance increase; as the frequency decreased to 6.25 Hz and 1.47 Hz, the conductance gradually decreased and stabilized; when the frequency was restored to 6.25 Hz, the conductance rose again. We also characterized the retention of ΔG after stimulation at different frequencies in the pre-post-pre mode, and the conductance of the device didn’t exhibit an obvious decrease over a period of 1000 s. (Supplementary Fig. 19). This frequency-dependent plasticity with history dependence reveals a sliding threshold mechanism, where synaptic potentiation or depression depends not only on the current frequency but also on the prior stimulation history, fully reflecting the key features of the BCM learning rule. A comparative analysis further confirms that the device exhibits biologically plausible time scales and plasticity rules (Table S1), and the benchmarking results highlight its notable advantages over existing 2D ferroelectric/antiferroelectric memristors (Table S2).

To emulate the biological functionalities of nociceptors, which are specialized sensory neurons responsible for detecting harmful mechanical, thermal, or chemical stimuli, we systematically investigated the pain-perception-mimicking behavior of memristive devices based on the CBPS relaxor AFE material. These nociceptive functionalities include not only threshold-triggered responses to intense stimuli, but also dynamic behaviors such as sensitization under repeated stimulation and desensitization after stimulus withdrawal. Figure 4a schematically illustrates the biological pain-perception pathway, where harmful stimuli are detected by peripheral sensory terminals (e.g., mechanoreceptors or thermoreceptors), transmitted through dorsal root ganglia to the spinal cord, and ultimately perceived in the cerebral cortex, triggering protective responses32,58,59. To mimic this process, we applied periodic voltage pulses with increasing amplitudes to the CBPS device and monitored the current response (Fig. 4b). At low input voltages, the device remained in a low-conductance state, reflecting an inert or adaptive phase. However, once the voltage surpassed a certain threshold, a sharp increase in current was observed. A threshold current of 2 μA was defined as the reference pain threshold, analogous to the excitability threshold of biological nociceptors. Below this value, the device remained quiescent; once exceeded, the response was regarded as a pain triggering event. This demonstrates the CBPS device’s ability to detect harmful stimuli with high sensitivity, emulating biological nociceptive behavior. To investigate sensitization, a phenomenon where previous strong stimuli enhance subsequent response, we applied pairs of pulses to the device. In each pair, the first pulse had increasing amplitudes ranging from 2.0 to 3.2 V, while the second pulse maintained a constant amplitude. As shown in Fig. 4c, the current response to the second (unchanged) pulse increased significantly with higher amplitudes of the first pulse, indicating a memory effect. This mimics pain sensitization, where a strong prior input heightens the system’s sensitivity to subsequent stimuli. A Pain Sensitization Index32,60 (PSI) was defined to quantify this behavior:

Sensitization Index = I 2nd / I 0 × 100% (1)

Here, I 2nd is the current induced by the second (detection) pulse, and I 0 is the reference current. As shown in Fig. 4e, PSI increases monotonically with the amplitude of the first pulse, validating the accumulation-driven sensitization in CBPS devices.

We further examined the pain desensitization behavior by applying a fixed high-amplitude sensitizing pulse (+3 V) followed by a constant lower-amplitude detection pulse (+2 V) with variable inter-pulse delay times Δt (20 ms to 320 ms). As illustrated in Fig. 4d, the response to the detection pulse gradually weakened as Δt increased. A Pain Desensitization Index (PDI) was defined as:

Desensitization Index = I 2nd / I 0 × 100% (2)

As shown in Fig. 4f, the pain desensitization index (PDI) decays exponentially with increasing Δt, indicating that prolonged intervals reduce excitability—analogous to the habituation behavior in biological nociceptors. In short intervals, the sensitizing pulse leaves the device in a highly excitable state, while longer delays allow relaxation, reducing the subsequent response. This emulates the biological desensitization process, where the nervous system becomes less responsive to repeated stimulation over time. Furthermore, Fig. 4g illustrates the effect of continuous stimulation on device behavior: under a prolonged train of identical electrical pulses, the output current gradually increases and eventually saturates, indicating an accumulation effect that mimics chronic pain sensitization observed in biological systems. This long-term potentiation of nociceptive response reflects the device’s ability to retain stimulus history and emulate persistent excitability. In summary, the CBPS memristive device not only demonstrates threshold-triggered responses to high-intensity stimuli but also reproduces dynamic sensitization, desensitization, and chronic excitation behavior. These features suggest its strong potential as a building block for artificial nociceptors in intelligent neuromorphic perception systems with reflexive and memory-like capabilities.

In neuromorphic computing systems, directly embedding memristive devices into deep neural network architectures presents a promising pathway toward efficient and compact intelligent information processing. Particularly for vision tasks such as image recognition, the high-density conductance states and synaptic plasticity of memristors can enable hardware-level integration of weight storage and forward inference, effectively reducing power consumption and latency61,62. To evaluate the practical applicability of the developed CBPS-based memristors in such neuromorphic architectures, a CNN model was implemented for face recognition tasks. As illustrated in Fig. 5a, the network comprises three convolution–pooling layers with kernel sizes of 32, 64, and 128, respectively, and 3 × 3 pooling operations. This is followed by two fully connected layers, and classification is performed via a Sigmoid activation function. The Olivetti Faces dataset, originally provided by AT&T Laboratories Cambridge, was used for this task, which consists of 400 grayscale images (64 × 64 pixels) of 40 individuals, each with 10 face images. The first 8 images per person were used for training (320 samples), and the remaining 2 for testing (80 samples). Figure 5b presents the recognition accuracy curves for both the CBPS-mapped network and an ideal digital model over training epochs. Both models converge within ~100 iterations, achieving final test accuracies of 91.3% (CBPS) and 93.7% (ideal), with only a 2.4% performance gap. This result demonstrates that the discrete and limited conductance states of CBPS memristors still support effective weight mapping in CNNs, and that their physical tunability is well aligned with neural network learning rules. Correspondingly, the loss convergence curves in Fig. 5c show that the CBPS-mapped network maintains a consistent and smooth error reduction trajectory, closely matching that of the ideal model, further confirming the memristor’s stability and dynamic response during training. To examine classification fidelity in greater detail, confusion matrices of the two models were compared. As shown in Fig. 5d, the ideal model exhibits a clear diagonal-dominant matrix, indicating highly accurate identification across all 40 facial classes. The CBPS-based model in Fig. 5e also presents a diagonal-dominated pattern, confirming its overall effective recognition capability. Minor off-diagonal misclassifications were observed in classes with visually similar features, likely due to the limited number of distinct conductance states or non-ideal tuning precision in the CBPS devices. The conductance update characteristics, statistical analyses of conductance variation (ΔG) under potentiation and depression processes were performed. As shown in Fig. 5f, potentiation-induced ΔG values are relatively stable in the low-conductance regime, whereas saturation and larger fluctuations occur at higher conductance states. In contrast, the depression pathway shown in Fig. 5g exhibits more linear and uniform ΔG distribution, suggesting improved consistency and stability during weight weakening. This asymmetric behavior indicates that CBPS memristors are particularly well suited for precise weight suppression, an essential property for efficient synaptic weight adjustment during network training. To further extend its neuromorphic computing applications, we conducted CNN simulations based on the MNIST handwritten digit recognition task. The results show that the CBPS-CNN network exhibits stable and superior classification performance in this task (Supplementary Fig. 20). Together, these results validate the integration potential of CBPS-based memristors in CNN architectures for neuromorphic computing, achieving robust pattern recognition performance with device-level conductance mapping and biologically plausible learning dynamics.

Methods
Synthesis of CBPS crystals
High quality quaternary CBPS single crystals were synthesized via high Chemical Vapor Transport, CVT. Stoichiometric amounts of high-purity (all 99.99%) Cu, Bi, P, and Se were sealed in an evacuated 20 cm quartz ampule at 10⁻⁶ Torr and heated in a furnace. From ambient conditions, the temperature was increased to 870 °C within 48 h and held for ≈ 7 days. After heating, the furnace was allowed to cool to room temperature, upon which high-quality quaternary CBPS single crystals were obtained.

Characterization of CBPS
The crystal structure of quaternary CBPS was characterized using micro-XRD (Bruker D8 Discover). The elemental distribution of quaternary CBPS was characterized using a Quanta 400. The Raman spectra were performed by the WITec alpha300 Raman system with an excitation laser of 532 nm. The PFM measurements were performed using the Asylum Research MFP-3D and Cypher.

Device fabrication
The vertically structured sandwich devices were fabricated as follows: First, bottom electrodes consisting of Cr/Au (5 nm/45 nm) were patterned on a Si substrate with a 285 nm SiO₂ insulating layer using electron-beam lithography (DZS500) and deposited by thermal evaporation (DM400). Next, atomically thin CBPS flakes were mechanically exfoliated from bulk crystals and transferred onto the pre-patterned electrodes via a dry transfer method assisted by polydimethylsiloxane (PDMS). Pattering of CBPS was subsequently performed using reactive ion etching (RIE) (SHL 100u-RIE, Beijing SHL Semi. Equipment Co., Ltd.) to define the device area. Finally, top Cr/Au (5 nm/45 nm) electrodes were defined using a second electron-beam lithography step (DZS500) followed by thermal evaporation (DM400), completing the vertical device structure.

Device measurement
Measurements were performed under ambient vacuum conditions using a Keithley 4200A-SCS and Keithley 2636B semiconductor parameter analyzer in a probe station (CRX-6.5, LakeShore). The ferroelectric measurement was performed by ferroelectric analyzer (Huace FE-2000 Analyzer) in a probe station (CRX-6.5, LakeShore).

Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability
The codes that support the findings of this study are available from the corresponding author upon reasonable request.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors present a comprehensive and well-structured investigation into a memristive system based on the 2D relaxor\nantiferroelectric material CBPS, demonstrating polymorphic neuromorphic functionalities including BCM-type plasticity,\nnociceptor-like response, and in-memory computing capability. The study combines experimental validation, synaptic\nemulation, and CNN-level integration, and provides substantial insight into the material’s electrical behavior driven by Cu⁺\nion displacement. However, while the results are promising, I recommend major revisions to enhance the scientific rigor and\nclarity before publication consideration. Below are comments for the authors’ reference:\n1. The introduction effectively outlines the limitations of von Neumann architectures (e.g., serial processing, power\ndissipation) and positions neuromorphic computing as a solution. However, it lacks a specific discussion on why AFE\nmaterials, particularly CBPS, are superior to ferroelectric or other 2D materials for neuromorphic applications, limiting the\njustification for the material choice.\n2. Cu migration is strongly temperature-dependent. Have the authors investigated temperature effects on switching behavior\nor relaxation timescales?\n3. Neuromorphic systems are sensitive to noise. Are the observed states stable against low-frequency noise or random\ntelegraph noise? Please address or discuss.\n4. The device yield exceeds 90% (Figure S9), which is excellent. However, variability across multiple devices—such as\ndifferences in switching thresholds, endurance, and multilevel stability—is not detailed. Providing statistical data or\ndistribution histograms would reinforce the robustness of the fabrication process.\n5. While the material exhibits desirable synaptic functions, the biological plausibility (e.g., spike timing, plasticity rules) is not\nrigorously discussed. Including a comparative analysis would contextualize the device's performance relative to biological\nsystems.\n6. It is imperative to ensure that the axis labels, tick labels, and various annotations in the figures are clear and legible.\nFigure 2d and 2e lack the legend for the three curves of current.\nThe special legend for the triplet-pulse sequence in the conductance change heatmaps in Figure 3d and 3e should\ncorrespond to that in Figure 3c. If so, it should be shown in Figure 3c and the relationship explained in the figure caption.\nFigure 5b and 5c lack the legends for the two curves.", "reviewer_2": "In their work entitled \"Polymorphic Functionalization Driven by Ion Displacement-Induced Antiferroelectric Ordering in\nCuBiP2Se6\", Yang et al. characterize the applicability of patterned relaxor antiferroelectric CuBiP2Se6 for neuromorphic\ncomputing. To accomplish this, they characterize the structure and quality of their materials, establish antiferroelectric\nperformance, examine synaptic behavior, and carry out essential tests to benchmark performance. The work is thorough and\nof high importance. There are several edits that the authors should consider before publication is considered:\n1. There are several low-intensity peaks present in the XRD pattern in figure S2. Are these from the CBPS? If so, they\nshould be indexed or discussed in the figure caption. If not, is there another phase that they correspond to?\n2. What do the blue and red colors correspond to in the labels in the Raman spectrum in figure S3? Additionally, the authors\nshould provide literature references for the vibrational modes for CBPS in the main text (line 129).\n3. When describing the CBPS resistance characteristics in the main text (lines 194 - 204), the authors should include explicit\ndetails regarding the measurement parameters. For example, what was the read voltage of the ON/OFF conductance ratio?\nSome of this information is available in figure captions, but it is best if it is included in the main text. The same information\nshould be included for HRS and LRS.\n4. How are the authors defining yield in line 207? Are 90% of the arrayed devices insulative? Or is there and ON/OFF\nconductance ratio that is used to calculate this yield?\n5. Approximately how many cycles did CBPS devices last during testing before dielectric breakdown? The authors should\ninclude a brief description of endurance.\n6. The authors should define acronyms LTD and LTP in the main text.\n7. Have the authors invented the sensitization idexes (lines 309 and 318)? If not, please include some citations to provide\nbackground for these metrics.\n8. In Figure 5, labels are needed for the red and blue data sets in panels (b) and (c).", "reviewer_3": "This manuscript presents a detailed investigation of vertical memristors using the 2D antiferroelectric material CuBiP2Se6\n(CBPS). Although the scale and uniqueness are praiseworthy, the current version is undermined by a few flaws, such as\ninconsistent fabrication claims, a lack of statistical depth, inadequate structural validation, and\ninadequate mechanistic clarity. To raise the manuscript to a publishable level, these problems must be fixed.\n1. How do the authors verify the single-crystal nature of CBPS flakes beyond micro-XRD (The crystal structure of quaternary\nCBPS was characterized using micro-XRD (Bruker D8 Discover))? Provide conclusive evidence (e.g., single-crystal XRD,\nLaue diffraction, or SAED patterns) to support the claim of single crystallinity.\n2. The devices are fabricated using mechanical exfoliation of CBPS flakes, which contradicts the single-crystal claim. Also,\nthere is no mention of flake thickness screening, alignment quality, or inter-device isolation in the 5×5 array.\n3. Clarify the rationale behind exfoliation from “single crystals.” Include flake thickness control, alignment strategy, and\npotential contamination from PDMS transfer. Add discussion on long-term device stability under ambient conditions.\n4. If the CBPS is truly single-crystalline, why is exfoliation used? How was flake uniformity verified prior to electrode\ndeposition? What steps were taken to prevent cross-talk in the device array?\n5. The authors propose that Cu⁺ ion displacement underlies all functions—AFE switching, BCM-type learning, and\nnociceptor-like responses. However, these mechanisms are not clearly isolated or experimentally differentiated.\n6. What experimental data confirms that the nociceptor behavior and BCM learning are governed by Cu⁺ displacement and\nnot generic ionic drift or Joule heating?\n7. Include performance statistics (mean ± SD) across multiple devices for key metrics like ON/OFF ratio, ΔG, and synaptic\nresponse. Discuss batch-to-batch variability.\n8. What is the variation in switching voltage and conductance across multiple CBPS memristors?\n9. The double-loop in Figure 1f is said to reflect AFE behavior, but it also resembles relaxor-like switching. Were frequency-\nor temperature-dependent P–E loops performed to confirm antiferroelectricity versus relaxor response?\n10. Are the ON/OFF states stable over time and across different devices? Is the switching behavior attributed to Cu⁺\nmigration or polarization?\n11. The ΔG–frequency trend suggests BCM but lacks quantitative fitting or theoretical modeling. Can the authors provide\nretention data or time-resolved analysis to confirm long-term memory and activity-dependent threshold shifting?\n12. Only one dataset (Olivetti) is used for CNN validation. There’s no discussion of endurance or performance under online\nlearning or drift. Test on an additional dataset or apply cross-validation. Discuss the role of conductance nonlinearity,\nsaturation, or variability in CNN accuracy.\n13. How do conductance nonlinearity and device drift affect learning and inference stability in hardware-implemented neural\nnetworks?\n14. Include a table or discussion comparing performance metrics (e.g., conductance tunability, LTP/LTD depth, endurance,\nnoise margin) with existing literature.\n15. How reproducible is the Cu⁺-induced AFE–FE transition across different CBPS batches and devices?\n16. What physical or chemical signatures differentiate synaptic modulation from ionic drift or dielectric breakdown in these\ndevices?\n17. Can the authors demonstrate scalable, CMOS-compatible fabrication beyond manual exfoliation and lithography?"}, "author_response": "Point-by-Point Response to Reviewers’ Comments\nWe acknowledge the reviewers for your carefully reading our manuscript and providing\npositive comments on our work. According to the reviewers' comments, we have made point-\nby-point revisions and provided more detailed data to improve its readability. We have\nsupplemented the experiments and revised the manuscript to provide sufficient experimental\ndata and more evidence to validate our conclusions. We have marked the response to reviewers'\ncomments in blue and the corresponding revisions to the questions in red.\nReviewer #1\nThe authors present a comprehensive and well-structured investigation into a memristive\nsystem based on the 2D relaxor antiferroelectric material CBPS, demonstrating\npolymorphic neuromorphic functionalities including BCM-type plasticity,\nnociceptor-like response, and in-memory computing capability. The study combines experimental\nvalidation, synaptic emulation, and CNN-level integration, and provides substantial\ninsight into the material’s electrical behavior driven by Cu⁺ ion displacement. However,\nwhile the results are promising, I recommend major revisions to enhance the scientific\nrigor and clarity before publication consideration. Below are comments for the authors’\nreference:\nReply:We appreciate the reviewer for the positive comments on our work. Meanwhile, the\nreviewer provided detailed and constructive suggestions, which greatly improved the\nreadability of the manuscript. With the constructive comments of the reviewer, the manuscript\nhas been largely improved. In the following, we have addressed all comments point-by-point\nand revised the manuscript correspondingly. We hope that the revised manuscript will address\nall the concerns from the reviewer.\nComment 1:\nThe introduction effectively outlines the limitations of von Neumann architectures (e.g.,\nserial processing, power dissipation) and positions neuromorphic computing as a solution.\nHowever, it lacks a specific discussion on why AFE materials, particularly CBPS, are\nsuperior to ferroelectric or other 2D materials for neuromorphic applications, limiting the\njustification for the material choice.\nReply: Thank the reviewer for this constructive comment. We fully agree that the original\nmanuscript lacked a sufficient explanation of the rationale behind selecting AFE materials-\nespecially CBPS for neuromorphic applications. In response, we have revised the introduction\nsection of the manuscript to explicitly address this point and provide a more comprehensive\njustification. The newly added content highlights the advantages of relaxor AFE materials in\nenabling energy-efficient, history-dependent, and analog synaptic modulation, as well as the\nspecific material benefits of CBPS, such as its van der Waals layered structure, CMOS\ncompatibility, and favorable interfacial properties.\nThe revised paragraph in the Introduction is shown below:\n“Compared with conventional ferroelectric (FE) systems, antiferroelectric (AFE)\nmaterials—with their near-zero remanent polarization and antiparallel dipole configurations—\ncan achieve analog, continuously tunable conductance modulation, making them ideal\ncandidates for energy-efficient neuromorphic devices35, 36, 37, 38, 39. Their unique polarization-\nswitching mechanism further endows them with higher breakdown fields36, 40, faster\npolarization switching speeds41, 42, lower energy dissipation36, 43, and excellent fatigue\nresistance44, 45. In particular, relaxor-type AFE materials exhibit gradual polarization responses\nunder external electric fields and slowly relax back once the electric field is removed46, 47. Such\ndynamical characteristics more closely resemble the time-dependent and history-sensitive\nbehaviors of biological synapses, rendering them especially suitable for hardware\nimplementation of sliding-threshold regulation in BCM-type plasticity. To the best of our\nknowledge, the van der Waals layered compound CuBiP₂Se₆ (CBPS) is the first 2D AFE\nsemiconductor and have outstanding properties: it possesses intrinsic antiferroelectric order\nwith antiparallel dipoles, enabling nonlinear, history-dependent, and analog conductance\nmodulation. Furthermore, its dangling-bond-free surfaces and atomically thin geometry ensure\nexcellent interfacial quality, scalability, and compatibility with silicon-based CMOS\ntechnologies48, 49, 50, 51, 52, distinguishing CBPS as a promising platform for mimicking complex\nsynaptic functionalities in neuromorphic applications. ”\nComment 2:\nCu migration is strongly temperature-dependent. Have the authors investigated\ntemperature effects on switching behavior or relaxation timescales?\nFigure R1. P-E hysteresis loops of vertical CBPS devices measured from 210 K to 330 K with\na step of 10 K, illustrating the temperature-dependent evolution of polarization behavior.\nReply: We sincerely thank the reviewer for this valuable comment.The polarization behavior\nin CBPS devices is mainly driven by electric field-dependent Cu⁺ displacement (weak electric\nfield) or migration (strong electric field). We performed P-E hysteresis measurements over the\nrange of 210–330 K (Figure R1). The results consistently show typical AFE double hysteresis\nloops throughout the entire temperature window, indicating that although the migration rate of\nCu⁺ ions varies with temperature, the induced polarization mode remains robust. While slight\nvariations in loop shape and remanent polarization are observed, the overall switching\ncharacteristics remain essentially unchanged. These results demonstrate that CBPS devices\nmaintain stable polarization responses driven by Cu⁺ migration or displacement in this\ntemperature range. The results have been added to the Revised Supporting Information (Figure\nS6).\nComment 3:\nNeuromorphic systems are sensitive to noise. Are the observed states stable against low-\nfrequency noise or random telegraph noise? Please address or discuss.\nReply: We thank the reviewer for the suggestion. The standard assessment of noise robustness\nin memristive/neuromorphic devices is to (i) record the current I(t) under a constant read\nvoltage bias and inspect the time trace for slow drift or two-level random telegraph noise (RTN)\njumps; and (ii) compute the current power spectral density (PSD) S(ƒ) (A2/Hz) from the same\nI\ndata via Welch/FFT, examine whether it follows a 1/ƒα (ideally α≈1), whether a high-frequency\nwhite-noise floor appears, and whether any Lorentzian RTN contribution produces a corner.\nWe tried our best to adopt a simple yet standard protocol: long time-series acquisition at a\nfixed read bias plus PSD analysis, applied to multiple programmed conductance states (see\nrevised Figure R2).\nProgrammed state: we first applied 10 identical write voltage pulses to set the initial state,\nthen added 5 extra pulses each step to obtain 10 distinct states; only the pulse count was varied.\nFor each state, noise was acquired immediately under the same read bias.\nTime domain (Figure R2b). After a short post-write relaxation, I(t) is stationary. We\nobserve only small fluctuations, with no discernible two-level jumps or long-term drift-features\ninconsistent with RTN-dominated behavior.\nFrequency domain (Figure R2c). In the low-frequency range ƒ≈0.03-1Hz, S(ƒ) follows\nI\n1/ƒα with fitted slope −α ≈−1. Curves for different states are nearly parallel on log–log axes;\namplitudes scale mildly with the DC current. No Lorentzian bulges or corners characteristic of\n∼\nRTN are observed.\nUnder this conditions, the programmed conductance states are robust against LFN, and\nRTN is not dominant. The 1/ƒ spectral signature (α≈1) and the stationary I(t) traces are\nconsistent. We have added these results in the Revised Supporting Information S14.\nFigure R2. (a) The read current (I) as a function of time (t) for different programmed states.\nThe voltage pulse number of increasing step is 5. (b) Retention characteristics of the\nprogrammed states, showing stable separation of all conductance levels over 10³s without\nnoticeable degradation (10-55 writing voltage pulse number with the amplitude of 3 V and the\nwidth of 20 ms). (c) 1/ƒαbehavior (α ≈ 1) from 0.03-1 Hz.\nComment 4:\nThe device yield exceeds 90% (Figure S9), which is excellent. However, variability across\nmultiple devices—such as differences in switching thresholds, endurance, and multilevel\nstability—is not detailed. Providing statistical data or distribution histograms would\nreinforce the robustness of the fabrication process.\nReply: We sincerely thank the reviewer for this valuable comment. To further confirm the\nrobustness and consistency of our devices, we carried out systematic measurements on a 5×5\ncrossbar array comprising 25 cells. All devices exhibited stable gradual I-V switching over >100\ncycles and consistent multi-pulse responses, as illustrated in Figures R3-4. These results\nconfirm that our fabrication process ensures reliable performance at the array level.\nIn addition, we evaluated three independent devices randomly selected from different\nfabrication batches. All devices maintained stable I-V characteristics over more than 1000\ncycles. Each device exhibited progressive and monotonic conductance modulation with clearly\ndistinguishable ≥10 conductance states, stable retention time exceeding 10³s, and reliable\nemulation of LTP/LTD behaviors, as shown in Figures R5-7.\nTaken together, these results demonstrate that our memristors exhibit high reproducibility\nand stability both at the device-array scale and across different fabrication batches. We have\nadded these results in the revised Supporting Information S11-S12 and S15-S17.\nFigure R3. I-V characteristics of 25 cells in a CBPS-based crossbar array over 100 cycles.\nFigure R4. Multi-pulse responses of 25 cells in a CBPS-based crossbar array.\nFigure R5. Morphology and memristive performance of CBPS devices (#device 1). (a) Gradual\nI-V hysteresis showing stable and reversible bipolar switching.The inset is the AFM image of\nthe sample used in device with a thickness of ~18 nm. (b) The gradually increasing writing\nvoltage amplitude from 3 V to 8 V at the same pulse interval and number (20 ms, 20 pulses)\nleads to a gradual rise in conductance. (c) Retention characteristics demonstrating stable\nseparation of all states over 10³ s without noticeable degradation (10-55 writing voltage pulse\nnumber with the amplitude of 3 V and the width of 20 ms). (d) Reliable emulation of synaptic\nplasticity, showing stable conductance potentiation (LTP) and depression (LTD) under repeated\nidentical pulse stimulation.\nFigure R6. Morphology and memristive performance of CBPS devices (#device 2). (a) Gradual\nI-V hysteresis showing stable and reversible bipolar switching.The inset is the AFM image of\nthe sample used in device with a thickness of ~23 nm. (b) The gradually increasing writing\nvoltage amplitude from 3 V to 8 V at the same pulse interval and number (20 ms, 20 pulses)\nleads to a gradual rise in conductance. (c) Retention characteristics demonstrating stable\nseparation of all states over 10³s without noticeable degradation (10-55 writing voltage pulse\nnumber with the amplitude of 3 V and the width of 20 ms). (d) Reliable emulation of synaptic\nplasticity, showing stable conductance potentiation (LTP) and depression (LTD) under repeated\nidentical pulse stimulation.\nFigure R7. Morphology and memristive performance of CBPS devices (#device 3). (a) Gradual\nI-V hysteresis showing stable and reversible bipolar switching.The inset is the AFM image of\nthe sample used in device with a thickness of ~26 nm. (b) The gradually increasing writing\nvoltage amplitude from 3 V to 8 V at the same pulse interval and number (20 ms, 20 pulses)\nleads to a gradual rise in conductance. (c) Retention characteristics demonstrating stable\nseparation of all states over 10³ s without noticeable degradation (10-55 writing voltage pulse\nnumber with the amplitude of 3 V and the width of 20 ms). (d) Reliable emulation of synaptic\nplasticity, showing stable conductance potentiation (LTP) and depression (LTD) under repeated\nidentical pulse stimulation.\nComment 5:\nWhile the material exhibits desirable synaptic functions, the biological plausibility (e.g.,\nspike timing, plasticity rules) is not rigorously discussed. Including a comparative analysis\nwould contextualize the device's performance relative to biological systems.\nReply: We sincerely thank the reviewer for the valuable comment. We have included a\nsystematic comparative analysis in the revised manuscript (see Table R1).\nShort-term plasticity (PPF): The PPF decay can be quantitatively described by a double-\nexponential function1,2:\n−∆𝑡/𝜏1 −∆𝑡/𝜏2\n𝑃𝑃𝐹(∆𝑡)= 𝐴1𝑒 +𝐴2𝑒\nFitting results yield a fast time constant τ₁ ≈ 53 ms and a slow time constant τ₂ ≈ 190 ms, which\nfall within the typical ranges of biological synapses (10-100 ms for the fast situation and 100\nms for the slow situation). This indicates that the device exhibits biologically plausible\ndynamics in short-term facilitation.\nSpike-timing-dependent plasticity (STDP): The device exhibits potentiation (LTP) for\nΔt > 0 and depression (LTD) for Δt < 0, forming a typical asymmetric STDP curve. The\neffective time window is approximately ±200 ms, consistent with the slow relaxation process\n(τ₂ ≈ 190 ms) of the device. Notably, broader STDP windows have also been reported in cortical\nand hippocampal synapses, ensuring the plausibility of this behavior.\nLong-term plasticity (LTP/LTD): The device shows progressive conductance\npotentiation and depression under repeated pulse stimulation, with the percentage of\nmodulation ranging from 50% to 200%, consistent with the typical weight changes observed in\nbiological synapses. The plasticity is activity-dependent and demonstrates good retention.\nBCM learning rule: Frequency-dependent plasticity measurements reveal a crossover\nfrom LTD to LTP at a critical frequency of ~15 Hz, which is highly consistent with the BCM\nmodification threshold observed in cortical neurons. Moreover, the threshold shifts with the\ninitial conductance state: high-conductance states tend to favor LTD, while low-conductance\nstates favor LTP, thereby reproducing the hallmark of the BCM theory.\nIn summary, we not only demonstrate multiple synaptic functionalities in our device but\nalso provide comparative evidence, showing that its time scales and plasticity rules closely\nmatch those of biological synapses. This strongly supports the biological plausibility of our\nwork and underscores its potential for neuromorphic computing applications. We added this\npart in the Revised Supporting Information Table S1.\nTable R1. Comparison between biological synapses and our device\nBiological synapses\nSynaptic function This work\n(typical)\nFast situation τ₁ ≈ 10-100 ms;\nShort-term plasticity (PPF) τ₁ ≈ 53 ms; τ₂ ≈ 190 ms\nSlow situation τ₂ ≈ 100 ms 1,2\nEffective time window ±40-\nSpike-timing-dependent 60 ms in hippocampal Effective time window ≈\nplasticity (STDP) synapses; ±200 ms in ±200 ms\ncortical/human neurons3,4\nLTP: current increased from\n2.75×10−8 A to 8.96×10−7 A\nafter 50 positive voltage\nPercentage of weight change\nLong-term plasticity spikes (+3160%)\namplitude typically at 50-\n(LTP/LTD) LTD: current decreased from\n200%5\n8.96×10−7 A to 3.69×10−8 A\nafter 50 negative voltage\nspikes (−95.9%)\nTransition from LTD to LTP Transition frequency ≈ 15 Hz;\nat θ M ≈ 10-20 Hz; sliding sliding threshold with\nBCM learning rule\nthreshold depending on conductance state (high-G →\nhistory of activity6,7 LTD, low-G → LTP)\nReference:\n[1] Zucker, R. S. et al. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355–405 (2002).\n[2] Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic\ncomputing. Nat. Mater. 16, 101–108 (2017).\n[3] Bi, G. Q. et al. Synaptic modifications in cultured hippocampal neurons: dependence on\nspike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18, 10464–10472\n(1998).\n[4] Froemke, R. C. et al. Spike-timing-dependent synaptic modification induced by natural\nspike trains. Nature 416, 433–438 (2002).\n[5] Bliss, T. V. et al. A synaptic model of memory: long-term potentiation in the hippocampus.\nNature 361, 31–39 (1993).\n[6] Bienenstock, E. L. et al. Theory for the development of neuron selectivity: orientation\nspecificity and binocular interaction in visual cortex. J. Neurosci. 2, 32–48 (1982).\n[7] Bear, M. F. et al. Long-term depression in hippocampus. Annu. Rev. Neurosci. 19, 437–462\n(1996).\nComment 6:\nIt is imperative to ensure that the axis labels, tick labels, and various annotations in the\nfigures are clear and legible.\nFigure 2d and 2e lack the legend for the three curves of current.\nThe special legend for the triplet-pulse sequence in the conductance change heatmaps in\nFigure 3d and 3e should correspond to that in Figure 3c. If so, it should be shown in Figure\n3c and the relationship explained in the figure caption.\nFigure 5b and 5c lack the legends for the two curves.\nReply: We thank the reviewer for suggesting in figure labeling. In the revised manuscript, we\nhave carefully reviewed and updated all figure annotations as follows:\nThe legends for the three current curves have been added in Figures 2d and 2e; The triplet-\npulse sequences used in Figures 3d and 3e are now clearly illustrated in Figure 3c, and the\ncorrespondence has been clarified in the figure caption; The legends for the two curves in\nFigures 5b and 5c have also been added; In addition, we have conducted a thorough inspection\nof all figures throughout the manuscript to ensure that axis labels, tick marks, and figure\ncaptions are clear and legible, and that a consistent visual style is maintained. We added this in\nthe Revised Manuscript.\n“Figure R8. Vertical memristor array based on CBPS and its synaptic characteristics.\n(a) Schematic of a 5×5 vertical memristor array. (b) The device shows clear bipolar switching\nbehavior under ±1.5 V, with an ON/OFF ratio >100 and stable operation over 1000 cycles. (c)\nPPF index as a function of pulse interval time ∆t. (d) The gradually increasing writing voltage\namplitude from 3 V to 8 V at the same pulse interval and number (20 ms, 20 pulses) leads to a\ngradual rise in conductance. (e) The gradually increasing pulses number from 10 to 50 at the\nsame writing voltage amplitude and interval (3V, 20ms) leads to a gradual rise in conductance.\n(f) Alternating potentiation and depression pulses cause reversible conductance changes,\nemulating long-term synaptic plasticity.”\n“Figure 3. Voltage- and spike-timing-dependent synaptic plasticity characteristics of\nCBPS-based memristors. (a) Long-term plasticity induced by cyclic modulation of pulse\namplitude. (b) Standard STDP behavior achieved by applying pre/post-synaptic pulse pairs with\nvarying time intervals. (c) Schematic of a triplet-pulse sequence with different timing\ncombinations (Δt₁, Δt₂) to explore higher-order STDP dynamics. This pulse scheme serves as\nthe input protocol for the conductance change heatmaps shown in Figures 3d and 3e,\ncorresponding to Post-Pre-Post and Pre-Post-Pre sequences, respectively. (d, e) Heatmaps of\nconductance change for two representative triplet patterns (Post-Pre-Post and Pre-Post-Pre),\nrevealing nonlinear dependence on spike timing and the interplay between LTP and LTD. (f)\nBCM-type plasticity recorded under various initial conductance states and stimulation\nfrequencies (1-50 Hz). The ΔG-frequency curve shows a non-monotonic trend, with LTD-to-\nLTP threshold shifting according to conductance history, consistent with the BCM sliding-\nthreshold rule. (g) Conductance variation under fixed pulse stimulation at different frequencies,\nshowing LTP and LTD behaviors.”\n“Figure R9. Integration of CBPS memristors into CNN architectures for neuromorphic\nface recognition. (a) Schematic diagram of the CNN architecture used for face recognition. (b)\nRecognition accuracy curves of the CBPS-mapped network (blue) and the ideal digital CNN\nmodel (red) during training on the Olivetti Faces dataset. (c) Loss curves of the two models\nduring training. (d, e) Confusion matrices of (d) the ideal model and (e) the CBPS-mapped\nCNN. (f) Statistical distribution of conductance change (∆G) during potentiation. (g)\nDistribution of ∆G during depression.”\nReviewer #2:\nIn their work entitled \"Polymorphic Functionalization Driven by Ion Displacement-\nInduced Antiferroelectric Ordering in CuBiP Se \", Yang et al. characterize the\n2 6\napplicability of patterned relaxor antiferroelectric CuBiP Se for neuromorphic\n2 6\ncomputing. To accomplish this, they characterize the structure and quality of their\nmaterials, establish antiferroelectric performance, examine synaptic behavior, and carry\nout essential tests to benchmark performance. The work is thorough and of high\nimportance. There are several edits that the authors should consider before publication is\nconsidered:\nReply: We greatly appreciate your positive evaluation of our work and the valuable suggestions\nyou provided. We are pleased to hear that you recognize the unique properties and potential of\nCBPS material for neuromorphic computing applications. We have carefully considered the\npoints you raised and have made the necessary revisions to improve the clarity and depth of the\nmanuscript. We believe that these revisions have enhanced the content of the manuscript and\nhave clarified key aspects of our research.\nThank you for giving us the opportunity to revise the manuscript, and we sincerely hope\nthat these revisions could meet your expectations.\nComment 1:\nThere are several low-intensity peaks present in the XRD pattern in figure S2. Are these\nfrom the CBPS? If so, they should be indexed or discussed in the figure caption. If not, is\nthere another phase that they correspond to?\nFigure R10. XRD pattern of CBPS single crystals.\nReply:We sincerely thank the reviewer for this valuable comment. The original XRD\npattern shown in Figure S2 was measured from a bulk CBPS sample, in which, besides\nthe main CBPS crystal planes, several low-intensity peaks were observed, likely\noriginating from minor secondary phases or surface residues. To further clarify this\nissue, we have added new results (Figure R10) in the revised Supporting Information\nS2: a freshly exfoliated shiny CBPS nanoflake was measured by XRD. The results show\nthat the dominant crystal planes can be clearly indexed to the (002), (004), and (006)\nplanes, indicating good crystallinity and pronounced preferred orientation, and no\nobvious extraneous peaks were detected. Moreover, the measured peak positions are in\nexcellent agreement with the standard CBPS data reported in the literature1, further\nconfirming the single-phase nature and high structural quality of the CBPS sample.\nReference:\n[1] Li, D. et al. Reconfigurable and giant bulk ionic photovoltaic effect in 2D quaternary metal\nthio(seleno)phosphates crystals. Adv. Mater. 37, 2509594 (2025).\nComment 2:\nWhat do the blue and red colors correspond to in the labels in the Raman spectrum in\nfigure S3? Additionally, the authors should provide literature references for the\nvibrational modes for CBPS in the main text (line 129).\nFigure R11. Raman spectrum of CBPS nano nanoflakes. The characteristic peaks are located\nat 111.7 cm⁻¹ (Bi-Se/Cu-Se lattice vibration), 135.3 cm⁻¹ (lattice vibration), 207.5 cm⁻¹\n(symmetric stretching vibration of the [P₂Se₆]⁴⁻ unit), 431.9 cm⁻¹ (P-Se bond stretching\nvibration), and 465.9 cm⁻¹ (P-Se bond stretching vibration). The peaks labeled in red correspond\nto out-of-plane vibrational modes, while those labeled in blue correspond to in-plane vibrational\nmodes.\nReply: We thank the reviewer for the valuable suggestion. In Figure S3, the peaks labeled in\nred correspond to the out-of-plane vibrational modes of the CBPS lattice, while those labeled\nin blue correspond to the in-plane vibrational modes. We have clarified this in the revised figure\ncaption (Figure R11). In addition, as recommended by the reviewer, we have added the relevant\nliterature references 1,2,3,4 at the line 129th of the manuscript to support these vibrational mode\nassignments.\n“The Raman spectrum of CBPS, as shown in Figure S3, exhibits several characteristic peaks\nat 111.7, 135.3, 207.5, 431.9, and 465.9 cm⁻¹. According to previous reports47, 53, 54, 55, the low-\nfrequency peaks at 111.7 and 135.3 cm⁻¹ are associated with lattice vibrations involving Bi-Se and\nCu-Se bonds, while the dominant peak at 207.5 cm⁻¹ is attributed to the symmetric stretching\nvibration of the [P₂Se₆] ⁴⁻ unit. The higher-frequency peaks at 431.9 and 465.9 cm⁻¹ correspond to\nthe P–Se bond stretching vibrations within the [P₂Se₆] ⁴⁻ group. Based on symmetry considerations,\nthe peaks labeled in red represent out-of-plane modes, whereas those in blue correspond to in-plane\nmodes.”\nReference:\n[1] Gave, M. A. et al. On the lamellar compounds CuBiP₂Se₆, AgBiP₂Se₆ and AgBiP₂S₆:\nantiferroelectric phase transitions due to cooperative Cu⁺ and Bi³⁺ ion motion. Inorg. Chem. 44,\n5293–5303 (2005).\n[2] Tawfik, S. A. et al. Van der Waals forces control the internal chemical structure of\nmonolayers within the lamellar materials CuInP₂S₆ and CuBiP₂Se₆. J. Phys. Chem. C 122,\n21811–21820 (2018).\n[3] He,W. et al. Record-high work-function p-type CuBiP₂Se₆ atomic layers for high-\nphotoresponse van der Waals vertical heterostructure phototransistor. Adv. Mater. 35, 2209995\n(2023).\n[4] Li, D. et al. Reconfigurable and giant bulk ionic photovoltaic effect in 2D quaternary metal\nthio(seleno)phosphates crystals. Adv. Mater. 37, 2509594 (2025).\nComment 3:\nWhen describing the CBPS resistance characteristics in the main text (lines 194 - 204), the\nauthors should include explicit details regarding the measurement parameters. For\nexample, what was the read voltage of the ON/OFF conductance ratio? Some of this\ninformation is available in figure captions, but it is best if it is included in the main text.\nThe same information should be included for HRS and LRS.\nReply: We thank the reviewer for this helpful suggestion. In the revised manuscript, we have\nexpanded the description in lines 194-204 to explicitly include the key measurement parameters.\nThis addition improves the completeness and clarity of the experimental description.\n“A vertical 5×5 memristor array was fabricated using the two-dimensional relaxor\nantiferroelectric material CBPS as the active layer, as schematically illustrated in Figure 2a,\nwith the optical image shown in Supplementary Figure S8. Electrical measurements reveal that\nthe device exhibits clear bipolar resistive switching under ±1.5 V voltage sweeps (Figure 2b),\nwhich can be attributed to field-driven vertical Cu⁺ ion displacement and the associated\npolarization modulation. A compliance current of 100 μA was applied during testing to avoid\nover-current damage, and the high-resistance state (HRS) and low-resistance state (LRS) values\nwere consistently extracted at a reading voltage of 0.5 V. Under these conditions, the device\nmaintains a high ON/OFF conductance ratio (>10²) over 1000 consecutive switching cycles\nwithout performance degradation, demonstrating excellent endurance. The statistical\ndistribution of HRS and LRS values over 1000 cycles, presented in Supplementary Figure S9,\nfurther confirms the stability and reproducibility of the resistive switching behavior. These\nresults highlight that the layered crystal structure and antipolar configuration of CBPS enable\nrobust and reversible switching characteristics.”\nComment 4:\nHow are the authors defining yield in line 207? Are 90% of the arrayed devices insulative?\nOr is there and ON/OFF conductance ratio that is used to calculate this yield?\nReply: We thank the reviewer for this valuable question. The yield (>90%) refers to the number\nof functional devices within the 5×5 array that exhibited reliable neuromorphic behavior during\nelectrical testing. Specifically, out of the 25 devices in the array, 23 showed reproducible bipolar\nresistive switching and successfully demonstrated synaptic-like characteristics, under the\ntesting conditions described. Devices that remained permanently insulating or failed to exhibit\nany consistent switching or synaptic response were excluded from the yield count. This\nclarification has now been added to the Revised manuscript to avoid ambiguity.\n“Additionally, the fabricated 5×5 array exhibited a device yield of 92% (23 out of 25\ndevices), which successfully demonstrated stable neuromorphic behaviors, including nonlinear\ncurrent–voltage characteristics and reliable multi-pulse conductance responses (Figures S11-\nS12), confirming the device’s reliability and compatibility with large-scale integration.”\nComment 5:\nApproximately how many cycles did CBPS devices last during testing before dielectric\nbreakdown? The authors should include a brief description of endurance.\nFigure R12. Consecutive I-V switching characteristics of the CBPS-based memristor.\nReply: We thank the reviewer for the constructive suggestion. In the original manuscript, we\nreported that the CBPS-based memristor exhibited stable bipolar resistive switching over 1000\nconsecutive cycles without performance degradation. To further validate the endurance, we\nperformed an additional 1000 switching cycles on the same device under identical measurement\nconditions, resulting in a total of 2000 cycles. As shown in Figure R12, the I–V characteristics\nremained highly stable throughout the extended testing, with no evidence of dielectric\nbreakdown or noticeable degradation, confirming the excellent endurance and long-term\nreliability of the device. We have added this in the Revised Supporting Information S10.\nComment 6:\nThe authors should define acronyms LTD and LTP in the main text.\nReply: We thank the reviewer for the helpful suggestion. In the revised manuscript, we have\ndefined the acronyms \"LTP\" and \"LTD\" at their first appearance in the manuscript as \"long-\nterm potentiation (LTP)\" and \"long-term depression (LTD)\", respectively.\nComment 7:\nHave the authors invented the sensitization idexes (lines 309 and 318)? If not, please\ninclude some citations to provide background for these metrics.\nReply: We sincerely thank the reviewer for raising this important question. The sensitization\nindexes used in our study are not newly invented, but are adapted from well-established\nmethodologies in recent neuromorphic nociceptor research1,2. Specifically, these indexes are\ncommonly employed to quantitatively evaluate pain-related behaviors such as sensitization\n(increased response to repeated or prolonged stimuli) and desensitization (reduced response\nafter adaptation). For example, sensitization index is typically defined as the ratio between the\ncurrent (or conductance) response after repeated noxious stimuli and the initial baseline\nresponse, while desensitization index is expressed as the relative decrease in response over\nsubsequent stimuli. These definitions provide a straightforward and reproducible way to\ncharacterize nociceptor-like behaviors in artificial synaptic devices. We have cited these\nreferences in the revised manuscript to clarify the origin, background, and validity of the\nsensitization-related metrics used in our work.\nReference:\n[1] Yang, H., et al. “Imitation of a Dual-Modal Synapse Based on a Hf ₀. ₅Zr ₀.₅O₂ Ferroelectric\nTunnel Junction for Neuromorphic Computing.” ACS Appl. Electron. Mater. 2024, 6(10), 7591–\n7599.\n[2] Wang, Y., et al. “Memristor-based adaptive neuromorphic perception in hardware.” Nat.\nCommun. 2024, 15, 48908.\nComment 8:\nIn Figure 5, labels are needed for the red and blue data sets in panels (b) and (c).\nReply: We thank the reviewer for pointing out the lack of data labeling in Figure 5b and 5c. In\nthe revised version, we have added clear legends to distinguish the red and blue data sets. The\nred curve corresponds to the ideal digital CNN model, while the blue curve represents the\nexperimental CBPS-mapped network. These changes improve the readability and clarity of the\nresults. Please see the Revised Figure 5 in the manuscript.\nFigure R13. Integration of CBPS memristors into CNN architectures for neuromorphic\nface recognition. (a) Schematic diagram of the CNN architecture used for face recognition. (b)\nRecognition accuracy curves of the CBPS-mapped network (blue) and the ideal digital CNN\nmodel (red) during training on the Olivetti Faces dataset. (c) Loss curves of the two models\nduring training. (d, e) Confusion matrices of (d) the ideal model and (e) the CBPS-mapped\nCNN. (f) Statistical distribution of conductance change (∆G) during potentiation. (g)\nDistribution of ∆G during depression.\nReviewer #3\nThis manuscript presents a detailed investigation of vertical memristors using the 2D\nantiferroelectric material CuBiP Se (CBPS). Although the scale and uniqueness are\n2 6\npraiseworthy, the current version is undermined by a few flaws, such as inconsistent\nfabrication claims, a lack of statistical depth, inadequate structural validation, and\ninadequate mechanistic clarity. To raise the manuscript to a publishable level, these\nproblems must be fixed.\nReply: We greatly appreciate the positive feedback from the reviewer and give us the\nopportunity to make revisions. The critical and constructive comments of the reviewer have\nbeen of great help to the improvement of our manuscript. We appreciate your insightful\ncomments on our research and understand that there are several concerns regarding the working\nmechanism that need further investigation. We have provided explanations and revisions in the\nfollowing point-by-point responses.\nComment 1:\nHow do the authors verify the single-crystal nature of CBPS nanoflakes beyond micro-\nXRD (The crystal structure of quaternary CBPS was characterized using micro-XRD\n(Bruker D8 Discover))? Provide conclusive evidence (e.g., single-crystal XRD,\nLaue diffraction, or SAED patterns) to support the claim of single crystallinity.\nFigure R14. (a) XRD pattern of exfoliated shiny CBPS nanoflakes, (b) SAED pattern of an\nexfoliated CBPS nanoflake.\nReply: We sincerely thank the reviewer for raising this important question. In the\noriginal manuscript, the XRD pattern shown in Figure S2 was obtained from a bulk\nCBPS crystal. In that measurement, in addition to the main CBPS crystal planes, several\nweak extra peaks were observed, which may originate from minor secondary phases or\nsurface residues in the bulk sample. This result may have, to some extent, misled the\nreviewer regarding the single-crystalline nature of CBPS.\nTo avoid ambiguity and to further verify the single-crystal nature of the nanoflakes\nused for device fabrication, we performed additional structural characterizations on\nexfoliated shiny CBPS nanoflakes. The XRD pattern (Figure R14a) of the nanoflakes\nshows only the (002), (004), and (006) crystal planes, which are highly consistent with\nthe layered structural characteristics of CBPS, indicating good crystallinity and strong\npreferred orientation along the c-axis. Meanwhile, the SAED (Figure R14b) results\nexhibit sharp and well-ordered hexagonal diffraction spots, with no polycrystalline\nrings or diffuse scattering signals observed, further confirming the single-crystalline\nnature of the nanoflakes. In summary, the supplementary XRD and SAED data provide\ndirect and conclusive evidence for the single-crystal quality of CBPS nanoflakes. We\nadded this in the Revised Supporting Information (Figure S2).\nComment 2:\nThe devices are fabricated using mechanical exfoliation of CBPS nanoflakes, which\ncontradicts the single-crystal claim. Also, there is no mention of nanoflake thickness\nscreening, alignment quality, or inter-device isolation in the 5×5 array.\nFigure R15. Representative I-V gradual conductance modulation characteristics with different\nthicknesses: (a) 10 nm, (b) 18 nm, (c) 23 nm, (d) 26 nm, and (e) 32 nm.\nReply: We thank the reviewer for the constructive comment. First, we would like to\nclarify that the CBPS nanoflakes used in this study were mechanically exfoliated from\nbulk single crystals grown by the CVT (Chemical Vapor Transport) method. The single-\ncrystalline nature of the bulk CBPS has been verified by XRD and SAED analyses.\nTherefore, the term “single crystal” in our manuscript refers to the source material,\nwhile the device nanoflakes were obtained from this bulk single crystal by mechanical\nexfoliation. This approach is widely used in 2D materials research1,2,3, and preserves\nthe intrinsic layered order and single-crystalline characteristics of the material.\nRegarding nanoflake thickness screening, we systematically tested devices\nfabricated from nanoflakes of different thicknesses (10 nm, 18 nm, 23 nm, 26 nm, and\n32 nm) (Figure R15a-e), and all devices were subjected to 1000 consecutive I-V cycles.\nThe results show that devices with thicknesses in the range of 18-26 nm exhibited the\nmost stable and reproducible gradual I-V conductance modulation. Devices at the\nthicknesses of 10 nm and 32 nm also displayed gradual modulation, but with narrower\nstability windows. For the fabrication of the 5×5 device array, we selected a 22 nm\nCBPS nanoflake, which lies within the optimal range. Importantly, this was a exfoliated\nnano nanoflake with large size that was transferred onto the pre-patterned 5×5 electrode\narray, ensuring that all 25 devices originated from the same nano nanoflake thickness.\nThis inherently guarantees uniformity in thickness, orientation, and crystal quality\nacross the array and avoids variations that may arise from different nano nanoflakes.\nAs for the fabrication process of the 5×5 array: (1) electron-beam lithography\n(EBL) was first used to define the bottom electrode patterns, followed by Cr/Au\ndeposition via thermal evaporation; (2) a 22 nm CBPS nanoflake obtained by\nmechanical exfoliation was then precisely transferred onto the pre-patterned electrode\nregion; (3) after the transfer of nano nanoflake, EBL and etching, were further employed\nto define the active device regions, thereby providing physical isolation between\nneighboring cells and ensuring that each device operated independently without\nelectrical crosstalk; (4) finally, EBL and metal deposition were used again to form the\ntop electrodes, completing the construction of the 5×5 array. This fabrication process\nguarantees that all devices within the array share the same thickness, orientation, and\ncrystalline quality, while also ensuring reliable inter-device isolation and\nreproducibility.\nReference:\n[1] Novoselov, K. S. et al. Electric field effect in atomically thin carbon films. Science 306,\n666–669 (2004).\n[2] Novoselov, K. S. et al. Two-dimensional atomic crystals. Proc. Natl Acad. Sci. USA 102,\n10451–10453 (2005).\n[3] Pizzocchero, F. et al. The hot pick-up technique for batch assembly of van der Waals\nheterostructures. Nat. Commun. 7, 11894 (2016).\nComment 3:\nClarify the rationale behind exfoliation from “single crystals.” Include nanoflake\nthickness control, alignment strategy, and potential contamination from PDMS transfer.\nAdd discussion on long-term device stability under ambient conditions.\nReply: We sincerely thank the reviewer for the valuable comments.\nFirst, CBPS is a typical van der Waals layered material, in which adjacent layers are\nbonded by weak van der Waals forces. This property allows high-quality thin nanoflakes to be\nmechanically exfoliated from bulk single crystals while preserving their ordered layered\nstructure and crystalline integrity. Therefore, the term “single crystal” in our manuscript refers\nto the bulk crystals grown by the CVT method, while the device nanoflakes were obtained from\nthis bulk single crystal by mechanical exfoliation, a widely adopted approach in 2D materials\nresearch.\nSecond, during device fabrication, we conducted a rigorous screening of the nanoflake\nthickness. These devices with different nanoflake thicknesses (10 nm, 18 nm, 23 nm, 26 nm,\nand 32 nm) were systematically tested under 1000 consecutive I-V switching cycles. The results\nindicate that devices with thicknesses in the range of 18–26 nm exhibit excellent stability and\nhighly reproducible gradual conductance modulation. In contrast, devices with relatively\nthinner or thicker flakes also display gradual conductance modulation, but their memory\nwindows in the I-V switching curves are narrower. Accordingly, for the fabrication of the 5×5\ndevice array, we selected a large CBPS nanoflake with a thickness of 22 nm and transferred it\nonto the pre-patterned electrode array. This method ensured that all 25 devices within the array\nshared the same thickness, orientation, and crystalline quality, avoiding variations that might\narise from using different nanoflakes. The AFM characterization of the CBPS nanoflake\n(Figure R16) confirmed that the transferred nanoflake exhibited a smooth surface without\nobvious wrinkles or organic residue from the PDMS carrier. In addition, a mild post-transfer\nannealing treatment (100 °C, 10 min) was applied to further improvement of the contact quality\nbetween the CBPS nanoflake and the electrodes.\nFigure R16. AFM images of mechanically exfoliated CBPS nanoflakes with different\nthicknesses. (a) A nanoflake with thickness of ~18 nm, (b) a nanoflake with thickness of ~23\nnm, and (c) a large-area nanoflake with thickness of ~22 nm used for the fabrication of the 5×5\ndevice array.\nFigure R17. Schematic diagram of dry transfer process of CBPS device assisted with\npolydimethylsiloxane (PDMS).\nThird, for array fabrication and alignment, electron-beam lithography (EBL) and metal\ndeposition were employed to define the electrode patterns with high precision, ensuring\naccurate alignment and minimizing crosstalk between neighboring cells. The detailed\nfabrication process is illustrated in Figure R17: (1) EBL was first used to define the bottom\nelectrode patterns on the substrate, followed by Cr/Au deposition via thermal evaporation;\n(2) large CBPS nanoflakes were mechanically exfoliated from bulk single crystals and picked up\nusing a transparent PDMS stamp1,2,3; (3) under assistance of optical microscope, the nanoflake\nwas accurately aligned and transferred onto the pre-patterned electrode region; (4) after transfer,\nEBL and etching were employed to define the active regions, thereby achieving physical\nisolation between adjacent cells and eliminating electrical crosstalk; (5) finally, a second round\nof EBL and metal deposition was used to fabricate the top electrodes, completing the 5×5\ndevice array. This process guarantees that all devices originate from the same nanoflake with\nuniform thickness, orientation, and crystal quality, while ensuring reliable device isolation and\nstable operation. We have added the process flow diagram in Revised Supporting Information\nS8.\nFinally, we systematically evaluated the environmental reliability of CBPS devices. After\nexposure of the ambient conditions for more than 100 days, the devices still exhibited stable\ngradual bipolar conductance modulation without degradation as shown in Figure R18a.\nMoreover, the I-V switching characteristics over 1000 cycles remained highly stable, with no\nsigns of dielectric breakdown or noticeable degradation (Figure R18b), confirming the\nexcellent long-term reliability of CBPS devices. These results have been included in the\nRevised Supporting Information S10.\nFigure R18. Consecutive I-V switching characteristics of the CBPS-based memristor.\nReference:\n[1] Kim, C. et al. Damage-free transfer mechanics of two-dimensional materials. NPG Asia\nMater. 13, 44 (2021).\n[2] Zhao, Y. et al. Large-area transfer of two-dimensional materials free of cracks,\ncontamination and wrinkles. Nat. Commun. 13, 4409 (2022).\n[3] Pham, P. V. et al. Transfer of 2D films: from imperfection to perfection. ACS Nano 18,\n14841–14876 (2024).\nComment 4:\nIf the CBPS is truly single-crystalline, why is exfoliation used? How was nanoflake\nuniformity verified prior to electrode deposition? What steps were taken to prevent cross-\ntalk in the device array?\nReply: We sincerely thank the reviewer for the valuable comments. CBPS is a graphene-like\nvan der Waals layered material. Similar to graphene, the adjacent layers of the CBPS are held\ntogether by weak van der Waals forces. This structural feature enables the mechanical\nexfoliation of high-quality, single-crystalline two-dimensional nanoflakes while preserving\ntheir layered order and crystalline integrity. Therefore, the mechanical exfoliation method from\nbulk single crystals is a widely adopted and effective way for device fabrication in 2D materials\nresearch.\nTo ensure the uniform thickness and surface quality of the selected nanoflakes, we first\nscreened candidate regions using optical microscopy and further confirmed the thickness and\nsurface flatness using AFM. The AFM images (Figure R16, Figure R19a) showed that the\nnanoflake surfaces were smooth and free wrinkles or contaminants, meeting the requirements\nfor subsequent device fabrication.\nDuring the fabrication of the device arrays, to prevent electrical crosstalk between\nneighboring devices, we employed EBL combined with an etching process to define the active\nregions of the transferred nanoflakes (Figure R17). This step physically isolates adjacent cells\nand ensures that each device operates independently (Figure R19b). During electrical\nmeasurements, each device was tested individually, with probe connections and measurement\nsetup applied only to the selected device. This fabrication and testing protocol effectively\neliminates potential electrical interference or crosstalk from adjacent devices. We have added\nthis in Revised Supporting Information S8.\nFigure R19. (a) AFM image of the transferred CBPS nanoflake on pre-patterned electrodes\nbefore etching. (b) Optical image of the fabricated 5×5 CBPS device array.\nComment 5:\nThe authors propose that Cu⁺ ion displacement underlies all functions—AFE switching,\nBCM-type learning, and nociceptor-like responses. However, these mechanisms are not\nclearly isolated or experimentally differentiated.\nReply: We thank the reviewer for the insightful comments. Theoretical studies have\ndemonstrated that the polarization behavior in CBPS could be primarily governed by field-\ninduced Cu⁺ ion displacement within the van der Waals layered structure 1,2. In our work, these\nmechanisms are experimentally verified by both the characteristic double hysteresis loops\nobtained from P-E measurements and the reversible domain contrast observed via PFM,\nconfirming the relaxor antiferroelectric nature of CBPS. Building on this intrinsic Cu⁺ ion\ndisplacement–polarization mechanism, we constructed CBPS-based memristor devices capable\nof emulating multiple neuromorphic functions. Notably, both the BCM learning rule and\nnociceptor-like behaviors are realized through continuous and programmable conductance\nmodulation under specific voltage pulse parameters. These functions share the same underlying\norigin—Cu⁺ ion displacement–driven polarization switching—which enables dynamic weight\nadaptation and threshold sliding analogous to biological synapses without the need for complex\nperipheral circuits. Therefore, the synaptic plasticity features such as potentiation/depression,\nsliding-threshold learning, and dynamic current responses are not independent effects, but\nrather emergent behaviors arising from the intrinsic polarization dynamics induced by Cu⁺ ion\ndisplacement.\nReference:\n[1] Tawfik, S. A. et al. van der Waals forces control the internal chemical structure of\nmonolayers within the lamellar materials CuInP₂S₆ and CuBiP₂Se₆. J. Phys. Chem. C 122,\n22675–22687 (2018).\n[2] Gave, M. A. et al. On the lamellar compounds CuBiP₂Se₆, AgBiP₂Se₆ and AgBiP₂S₆:\nAntiferroelectric phase transitions due to cooperative Cu⁺ and Bi³⁺ ion motion. Inorg. Chem.\n44, 5293–5303 (2005).\nComment 6:\nWhat experimental data confirms that the nociceptor behavior and BCM learning are\ngoverned by Cu⁺ displacement and not generic ionic drift or Joule heating?\nReply: We thank the reviewer for raising this critical question. The AFE behavior of CBPS\nprovides the physical foundation for realizing both the BCM learning rule and nociceptor-like\nfunctions. Under an external electric field, CBPS exhibits clear and reversible polarization\nswitching, which is intrinsically governed by the displacement of Cu⁺ ions. This mechanism\nenables the devices to achieve high controllability, multi-stable states, and gradual conductance\nmodulation—key attributes essential for mimicking biological synaptic functionalities.\nMoreover, theoretical studies 1,2 have explicitly demonstrated that the AFE polarization\ntransition in CBPS is driven by cooperative Cu⁺ ion displacement. This displacement is closely\nrelated to the unstable coordination of Cu⁺ ions in the quasi-octahedral voids and is\nstrongly influenced by the second-order Jahn–Teller (SOJT) effect, leading to local non-\ncentrosymmetric distortion and triggering polarization reversal. It is precisely this SOJT-\ndriven Cu⁺ ion displacement mechanism that gives CBPS its characteristic relaxor\nantiferroelectric properties.\nIn our work, we further validate this mechanism through the following experimental\nobservations:\nPolarization switching and reversible PFM response confirm field-controllable\npolarization: We observed typical double hysteresis P-E loops, and PFM measurements\nrevealed reversible phase-contrast changes under bipolar voltage sweeps. These results confirm\nthe reversible nature of polarization in CBPS, with good repeatability and directionality,\neffectively ruling out stochastic ionic drift or Joule-heating–driven mechanisms.\nConsistent LTP/LTD responses support synaptic plasticity capability: As shown in\nFigure 2f, the devices exhibit highly consistent conductance modulation under repeated\nLTP and LTD pulse trains, further ruling out stochastic ionic drift or Joule-heating–\ndriven mechanisms. Such gradual and bidirectional conductance evolution is difficult to\nachieve through generic ionic drift or unstable thermal effects, and is more plausibly attributed\nto the intrinsic polarization control enabled by Cu⁺ ion displacement.\nIn summary, we believe that the observed neuromorphic functionalities are not governed\nby generic electrochemical effects or Joule heating, but are intrinsically rooted in the\npolarization-regulating mechanism driven by Cu⁺ ion displacement under the influence of the\nSOJT effect in the CBPS lattice.\nReference:\n[1] Reimers, J. R. et al. Van der Waals forces control ferroelectric–antiferroelectric ordering in\nCuInP₂S₆ and CuBiP₂Se₆ laminar materials. Chem. Sci. 9, 7620–7627 (2018).\n[2] Gave, M. A. et al. On the lamellar compounds CuBiP₂Se₆, AgBiP₂Se₆ and AgBiP₂S₆.\nAntiferroelectric phase transitions due to cooperative Cu⁺ and Bi³⁺ ion motion. Inorg. Chem.\n44(15), 5293–5303 (2005).\nComment 7:\nInclude performance statistics (mean ± SD) across multiple devices for key metrics like\nON/OFF ratio, ΔG, and synaptic response. Discuss batch-to-batch variability.\nReply: We sincerely thank the reviewer for the valuable comments. First, we carried out\nsystematic tests on a 5×5 crossbar array. The results show that the devices exhibit stable bipolar\nswitching characteristics within the ±1.5 V voltage range and maintain reliable performance\nover more than 100 cycles (Figure R20). In addition, multi-pulse modulation experiments\nreveal that each device in the array exhibits progressive and monotonic conductance evolution\nunder pulsed stimulation, with clearly distinguishable conductance states (Figure R21). These\nresults confirm the high yield and robust stability of the devices at the array level. Furthermore,\nwe have supplemented results from three independent batches (Devices 1-3). All devices\nmaintained stable gradual I-V characteristics within the ±1.5 V range and remained reliable over\nmore than 1000 cycles. In all three batches, the devices exhibited progressive and monotonic\nconductance modulation with ≥10 clearly distinguishable conductance states, retention time\nexceeding 10³ s, and reliable emulation of LTP/LTD behaviors (Figures R22-R24). The\nconsistency among different batches further demonstrates the reproducibility of device\nperformance across fabrication runs. In summary, both at the array level and at the single-device\nlevel across multiple batches, our CBPS-based memristors exhibit excellent reproducibility,\nstable endurance, and long-term reliability. We have added these results in the Revised\nSupporting Information S11-S12 and S15-S17.\nFigure R20. I-V characteristics of 25 cells in a 5×5 CBPS-based crossbar array over 100 cycles.\nAll devices exhibit stable gradual bipolar switching throughout the cycling process with good\noverall uniformity, confirming the scalability and fabrication robustness of the memristor array.\nFigure R21. Multi-pulse responses of 25 cells in a 5×5 CBPS-based crossbar array. All devices\nexhibit progressive and monotonic conductance potentiation under identical pulse conditions,\nwith clearly distinguishable states and good uniformity across the array, confirming the\nscalability and synaptic plasticity of the memristor array.\nFigure R22. Morphology and memristive performance of CBPS devices (#device 1). (a)\nGradual I-V hysteresis showing stable and reversible bipolar switching.The inset is the AFM\nimage of the sample used in device with a thickness of ~18 nm. (b) The gradually increasing\nwriting voltage amplitude from 3 V to 8 V at the same pulse interval and number (20 ms, 20\npulses) leads to a gradual rise in conductance. (c) Retention characteristics demonstrating stable\nseparation of all states over 10³ s without noticeable degradation (10-55 writing voltage pulse\nnumber with the amplitude of 3 V and the width of 20 ms). (d) Reliable emulation of synaptic\nplasticity, showing stable conductance potentiation (LTP) and depression (LTD) under repeated\nidentical pulse stimulation.\nFigure R23. Morphology and memristive performance of CBPS devices (#device 2). (a)\nGradual I-V hysteresis showing stable and reversible bipolar switching.The inset is the AFM\nimage of the sample used in device with a thickness of ~23 nm. (b) The gradually increasing\nwriting voltage amplitude from 3 V to 8 V at the same pulse interval and number (20 ms, 20\npulses) leads to a gradual rise in conductance. (c) Retention characteristics demonstrating stable\nseparation of all states over 10³ s without noticeable degradation (10-55 writing voltage pulse\nnumber with the amplitude of 3 V and the width of 20 ms). (d) Reliable emulation of synaptic\nplasticity, showing stable conductance potentiation (LTP) and depression (LTD) under repeated\nidentical pulse stimulation.\nFigure R24. Morphology and memristive performance of CBPS devices (#device 3). (a)\nGradual I-V hysteresis showing stable and reversible bipolar switching.The inset is the AFM\nimage of the sample used in device with a thickness of ~26 nm. (b) The gradually increasing\nwriting voltage amplitude from 3 V to 8 V at the same pulse interval and number (20 ms, 20\npulses) leads to a gradual rise in conductance. (c) Retention characteristics demonstrating stable\nseparation of all states over 10³ s without noticeable degradation (10-55 writing voltage pulse\nnumber with the amplitude of 3 V and the width of 20 ms). (d) Reliable emulation of synaptic\nplasticity, showing stable conductance potentiation (LTP) and depression (LTD) under repeated\nidentical pulse stimulation.\nComment 8:\nWhat is the variation in switching voltage and conductance across multiple CBPS\nmemristors?\nReply: We thank the reviewer for this important question. It should be emphasized that,\nwhether plotted on a linear or logarithmic scale, the I-V characteristics of CBPS devices\nconsistently exhibit gradual conductance modulation, rather than the abrupt SET/RESET\ntransitions typically observed in filamentary RRAM (Figure R25). Consequently, these devices\ndo not possess a single, well-defined “switching” voltage.\nInstead, we evaluate device-to-device variation in terms of the operating-voltage window\nand the conductance-modulation characteristics. As shown in Figure R26, we systematically\ntested devices from three independent batches: All devices display stable, gradual, bipolar I-V\ncharacteristics within an operating window of ±1.5 V; Using the same pulsing protocol, the\ndevices exhibit progressive, monotonic conductance evolution with 10 clearly resolvable states;\nthe small dispersion in both I-V loops and programmed conductance levels indicates good\ndevice-to-device and batch-to-batch reproducibility.\nIn summary, CBPS memristors do not rely on abrupt “switching voltages.” Instead, they\nmaintain a highly consistent analog modulation window and well-resolved conductance states\nacross multiple devices with negligible variation.\nFigure R25. (a) Linear-scale I-V curves. (b) Logarithmic-scale I-V curves.\nFigure R26. (a-c) Gradual bipolar I-V characteristics measured from three representative\ndevices, showing reproducible switching behavior within ±1.5 V with negligible variation. (d-\nf) multi-pulse conductance modulation of the same devices, demonstrating 10 stable and\ndistinguishable conductance states with good reproducibility across devices with negligible\nvariation.\nComment 9:\nThe double-loop in Figure 1f is said to reflect AFE behavior, but it also resembles relaxor-\nlike switching. Were frequency- or temperature-dependent P-E loops performed to\nconfirm antiferroelectricity versus relaxor response?\nReply: We thank the reviewer for this valuable comment. To evaluate the temperature effect,\nwe performed P-E hysteresis measurements of CBPS devices over the range of 210-330 K\n(Figure R27). The results consistently show characteristic double hysteresis loops throughout\nthe entire temperature window, which are hallmarks of relaxor antiferroelectric (AFE) behavior.\nThis confirms the robustness of the intrinsic relaxor AFE nature of CBPS. The results have been\nadded to the Revised Supporting Information S6.\nFigure R27. P-E hysteresis loops of CBPS devices measured from 210 K to 330 K with a step\nof 10 K, illustrating the temperature-dependent evolution of polarization behavior.\nComment 10:\nAre the ON/OFF states stable over time and across different devices? Is the switching\nbehavior attributed to Cu⁺ migration or polarization?\nReply: We thank the reviewer for this question. To verify the stability of the conductance states\nacross different devices and fabrication batches, we performed systematic tests on devices from\nthree independent batches. In these experiments, each device was subjected to a stepwise pulse\nstimulation protocol (10-55 writing voltage pulse number with the amplitude of 3 V and the\nwidth of 20 ms). All devices exhibited 10 clearly distinguishable conductance states, which\nremained stable for over 1000 s without noticeable degradation (Figure R28). These results\ndemonstrate that the conductance modulation characteristics are highly stable and reproducible,\nboth at the single-device level and multiple batches.\nRegarding the switching mechanism, our analysis suggests that the gradual switching\nbehavior of CBPS memristors is primarily attributed to the reversible modulation of\npolarization. Under an applied electric field, the local displacement of Cu⁺ ions induce\nreorientation of the interlayer dipoles, enabling controllable polarization states and gradual\nconductance modulation.\nFigure R28. (a) Magnified view of the marked region in (b). (b-d) multi-pulse conductance\nmodulation of the different devices, demonstrating 10 stable and distinguishable conductance\nstates with good reproducibility across devices.\nComment 11:\nThe ΔG–frequency trend suggests BCM but lacks quantitative fitting or theoretical\nmodeling. Can the authors provide retention data or time-resolved analysis to confirm\nlong-term memory and activity-dependent threshold shifting?\nReply: We thank the reviewer for this valuable suggestion. To further validate the BCM-like\nbehavior1,2,3 and activity-dependent threshold shifting, we performed retention measurements\nof the frequency-dependent responses under the pre-post-pre mode (Figure R29). The results\nshow that at higher stimulation frequencies (22.4 Hz, 18.1 Hz), the devices exhibit ΔG > 0\n(potentiation/LTP), whereas at lower frequencies (8.6 Hz, 5.2 Hz), they exhibit ΔG < 0\n(depression/LTD). More importantly, the memory states induced by different frequencies\nremain stably separated over 1000 s, with only slight relaxation and without overlap or\ndisappearance. This clearly demonstrates the long-term memory capability of the devices.\nThese findings further confirm that our CBPS-based memristors exhibit BCM-like, activity-\ndependent threshold modulation together with stable long-term memory characteristics. We\nadded this in the Revised Supporting Information S19.\nFigure R29. Retention of the device after stimulation at different frequencies in the pre-post-\npre mode.\nReference:\n[1] Wang, Z. et al. Toward a generalized Bienenstock–Cooper–Munro rule for memristive\ndevices. Nat. Commun. 11, 1510 (2020).\n[2] Xiong, W. et al. Bioinspired artificial synapses with modifiable frequency threshold for\nadaptive learning. Adv. Funct. Mater. 29, 1807316 (2019).\n[3] Yang, Y. et al. Inducing triplet-STDP learning rules in a memristor-based synapse. Adv.\nFunct. Mater. 28, 1704455 (2018).\nComment 12:\nOnly one dataset (Olivetti) is used for CNN validation. There’s no discussion of endurance\nor performance under online learning or drift. Test on an additional dataset or apply\ncross-validation. Discuss the role of conductance nonlinearity, saturation, or variability in\nCNN accuracy.\nReply: We thank the reviewer for the valuable suggestion. Based on the open-source crossSim\nframework from Sandia National Laboratories, we additionally performed CNN simulations\nusing the MNIST handwritten digit dataset (28×28-pixel grayscale images). During training,\nwe used the experimentally measured ΔG-G relationship of our device to construct a lookup\ntable (LUT) model. The average conductance update extracted from the LUT was superimposed\nwith write noise to update the synaptic weights, thereby incorporating nonideal device\ncharacteristics such as conductance nonlinearity, saturation, and cycle-to-cycle variability into\nthe simulation. Using this approach, the CNN achieved a recognition accuracy of 94% on\nMNIST (Figure R30)), consistent with the Olivetti dataset results, demonstrating the\nrobustness of our device–algorithm co-design methodology. The results indicate that the\nlinearity of conductance modulation has a significant impact on classification accuracy: higher\nlinearity leads to more uniform average weight updates across different conductance ranges,\nresulting in smaller update errors; in contrast, conductance saturation at the high/low ends\ncauses inaccurate weight updates, hindering convergence to the optimal solution. In addition,\nwe incorporated sampling noise from the experimentally measured distribution into the weight\nupdates, which shows that cycle-to-cycle consistency also affects the final recognition\nperformance. We added this in the Revised Supporting Information S20.\nFigure R30. (a) Recognition accuracy as a function of training epochs on the MNIST dataset.\nThe CBPS-based devices (blue) exhibit a convergence trend similar to the ideal model (red),\nreaching a final accuracy of ~94%. (b) Loss curves of the two models during training. (c, d)\nConfusion matrices of (c) the ideal model and (d) the CBPS-mapped CNN.\nComment 13:\nHow do conductance nonlinearity and device drift affect learning and inference stability\nin hardware-implemented neural networks?\nReply: We thank the reviewer for the valuable comment. In hardware-implemented neural\nnetworks, the synaptic weights are represented by the conductance states of the device array.\nConductance drift after training can alter the stored weight matrix, which may significantly\ndegrade inference accuracy and network stability. During training, drift behaves as additional\nweight noise, reducing update precision and thereby affecting learning performance. However,\na moderate level of drift can help suppress overfitting and improve generalization. Conductance\nnonlinearity—the nonlinear dependence of conductance changes on initial conductance—can\nnegatively impact training, as it leads to inconsistent update magnitudes across different weight\nranges, introducing weight update errors. Saturation effects at the conductance extremes further\nexacerbate this issue by limiting the available dynamic range for weight tuning. Therefore, both\nnonlinearity and drift must be carefully considered in device modeling and compensated for via\ntraining algorithms or circuit-level calibration to ensure stable learning and inference\nperformance.\nComment 14:\nInclude a table or discussion comparing performance metrics (e.g., conductance tunability,\nLTP/LTD depth, endurance, noise margin) with existing literature.\nTable R2. Performance comparison highlighting the advantages of CBPS memristors in\nvoltage, endurance, retention, multistate capacity, and array integration.\nOperating\nDevice Array I-V Multistate Retention\nStructure voltage Ref.\ntype scale endurance memory time\n[V]\nThree- Single\nCIPS <5 -20,20 25s (4state) 1\nterminal device\nTwo- Single\nGr/CIPS 10 -2, 2 20 600s (4 state) 2\nterminal device\nTwo-\nAu/CIPS/Ti 3×3 100 -1.5,1.5 70 2000s (2state) 3\nterminal\nTwo- Single\nGr/MoS2/CIPS <10 -2, 2 15 5s (4 state) 4\nterminal device\nSnS2/h- Three- Single\n<10 -20,20 128 10000s (2 state) 5\nBN/CIPS terminal device\nThree- Single\nGr/CIPS/WSe2 <5 -6,6 21 50s (12 state) 6\nterminal device\nGr/CIPS/h- Three- Single\n<5 -80,80 16 1000s (2 state) 7\nBN/WSe2 terminal device\nCIPS/h-BN/ - Three- Single\n<10 64 100s (3state) 8\nIn2Se3\n𝛼\nterminal device\nTwo-\nCuCrP2S6 4×4 1000 -1,1 30 5000s (2 state) 9\nterminal\nCuCrP2S6 1T1M 2 × 3 100 -1,1 20 10000s (2state) 10\nTwo- This\nCBPS 5×5 >2000 -1.5,1.5 50 1000s（10 state）\nterminal work\nCIPS: CuInP S\n2 6\nReply: To benchmark the performance of our CBPS devices, we have summarized the key\nmetrics of representative ferroelectric/antiferroelectric memristors in Table R2. The CBPS\nmemristors exhibit the following notable advantages:\nEndurance: Stable operation over 2000 I-V cycles, superior to most reported 2D\nferroelectric/antiferroelectric devices (typically <1000).\nOperating voltage: A low operating window of ±1.5 V, significantly smaller than multi-\nterminal CIPS heterostructures (±20-80 V), and comparable to state-of-the-art 2D memristors.\nMultistate capability: Up to ~50 well-distinguishable conductance states, outperforming\nmost reported counterparts.\nRetention: Excellent retention performance, with ≥10 conductance states remaining stable\nfor 10³ s.\nArray demonstration: A 5×5 crossbar array was successfully fabricated and\ncharacterized, confirming the scalability of CBPS for integrated neuromorphic applications.\nIn summary, CBPS simultaneously combines low-voltage operation, excellent endurance\nand retention, robust multistate capability, and achieves functional demonstration at the array\nlevel, thereby showing significant advantages over existing 2D ferroelectric/antiferroelectric\nmemristors. We added this in the Revised Supporting Information Table S2.\nReference:\n[1] Shang Z, et al. Ferroelectric polarization enhanced optoelectronic synaptic response of a\nCuInP S transistor structure. ACS Nano 18, 30530–30539 (2024).\n2 6\n[2] Liu Y, et al. CuInP S -based electronic/optoelectronic synapse for artificial visual system\n2 6\napplication. Adv. Funct. Mater. 34, 2306945 (2024).\n[3] Li B, et al. An electronic synapse based on 2D ferroelectric CuInP S . Adv. Electron. Mater.\n2 6\n6, 2000760 (2020).\n[4] Wang Z, et al. CuInP S heterojunction based visible range optoelectronic synapse with\n2 6\nfemtojoule energy consumption. Adv. Mater. Technol. 10, 2401589 (2025).\n[5] Wang P, et al. Integrated in-memory sensor and computing of artificial vision based on full-\nvdW optoelectronic ferroelectric field-effect transistor. Adv. Sci. 11, e2305679 (2024).\n[6] Cheng J, et al. Applying the wake-up-like effect to enhance the capabilities of two-\ndimensional ferroelectric field-effect transistors. ACS Appl. Mater. Interfaces 16, 24987–24998\n(2024).\n[7] Cheng J, et al. 2D memory enabled by electrical stimulation-induced defect engineering for\ncomplicated neuromorphic computing. Adv. Funct. Mater. 35, 2416333 (2025).\n[8] Das B, et al. Artificial visual systems fabricated with ferroelectric van der Waals\nheterostructure for in-memory computing applications. ACS Nano 17, 21297–21306 (2023).\n[9] Ma Y, et al. High-performance van der Waals antiferroelectric CuCrP S -based memristors.\n2 6\nNat. Commun. 14, 7891 (2023).\n[10] Ma Y, et al. Van der Waals engineering of one-transistor-one-ferroelectric-memristor\narchitecture for an energy-efficient neuromorphic array. Nano Lett. 25, 2528–2537 (2025).\nComment 15:\nHow reproducible is the Cu⁺-induced AFE-FE transition across different CBPS batches\nand devices?\nReply: We thank the reviewer for this valuable comment. First, it should be pointed out that no\nwell-defined AFE-FE phase transition exists in CBPS devices. Under strong electric fields, the\npolarization states exhibited in the P-E loops of CBPS can be attributed to the tendency of\npolarization domains to align along the direction of the applied field. This phenomenon is\nreferred to as the electric-field-induced polarization state, which has likewise been observed in\nnumerous traditional AFE materials1,2,3. To evaluate the reproducibility of the Cu⁺-induced\nAFE-FE transition, we measured the P-E hysteresis loops of four representative CBPS devices.\nAs shown in Figure R31, all devices consistently exhibit the characteristic double hysteresis\nloops of the reversible P-E switching, confirming that the Cu⁺-driven polarization switching is\nrobust, stable, and reproducible. We added this in the Revised Supporting Information S7.\nFigure 31. P-E hysteresis loops of four CBPS devices with different thicknesses (18 nm, 23\nnm, 26 nm, and 32 nm).\nReference:\n[1] Tagantsev, A. K. et al. The origin of antiferroelectricity in PbZrO₃. Nat. Commun. 4, 2229\n(2013).\n[2] Zhang, M.-H. et al. Electric-field-induced antiferroelectric to ferroelectric phase transition\nin polycrystalline NaNbO₃. Acta Mater. 200, 127–135 (2020).\n[3] Mani, B. K., Lisenkov, S. & Ponomareva, I. Finite-temperature properties of\nantiferroelectric PbZrO₃ from atomistic simulations. Phys. Rev. B 91, 134112 (2015).\nComment 16:\nWhat physical or chemical signatures differentiate synaptic modulation from ionic drift\nor dielectric breakdown in these devices?\nReply: We thank the reviewer for raising this critical question. In our study, the synaptic\nplasticity behaviors observed in CBPS-based devices—including LTP/LTD and BCM\nlearning—originate from Cu⁺ ion-driven polarization modulation rather than generic ionic drift\nor dielectric breakdown. This distinction is supported by multiple experimental observations:\n1.cycling stability: The I-V characteristics remain smooth and stable across repeated\nmeasurements, with no evidence of abrupt current jumps or breakdown events, thereby\nexcluding breakdown-dominated mechanisms.\n2.Gradual and repeatable conductance modulation: The device exhibits continuous\nand reproducible conductance evolution under repeated stimulation pulses. The progressive\nnature of the LTP/LTD curves reflects a controlled and reversible modulation process, which is\ninconsistent with random, irreversible conductive filament formation typically seen in ionic\ndrift or breakdown phenomena.\n3.PFM and P-E evidence for polarization-controlled behavior: We measured\ncharacteristic double-loop P-E hysteresis curves and observed reversible phase contrast\nchanges in PFM under applied bias. These results directly link the conductance modulation to\nreversible polarization states, reinforcing that the underlying mechanism is governed by field-\ninduced Cu⁺ ion displacement within the CBPS lattice.\nTaken together, these findings strongly indicate that the neuromorphic behaviors in our\ndevices are not driven by conventional ionic drift or Joule heating, but rather by the intrinsic\npolarization modulation mechanism driven by Cu⁺ ion displacement. This mechanism provides\nimplementing reliable and bioinspired synaptic functionalities in hardware.\nComment 17:\nCan the authors demonstrate scalable, CMOS-compatible fabrication beyond manual\nexfoliation and lithography?\nReply: We sincerely thank the reviewer for the valuable comments. The main objective of this\nwork is to elucidate the intrinsic polarization dynamics and neuromorphic functionalities of\nCBPS. To ensure material integrity and fabrication precision, we employed mechanical\nexfoliation and EBL to prepare high-quality prototype devices at the proof-of-concept stage.\nWe fully agree with the reviewer that scalable and CMOS-compatible fabrication processes are\ncrucial for future device integration. It is worth emphasizing that CBPS shares structural\nsimilarity with other van der Waals layered chalcogenides (e.g., transition metal\ndichalcogenides), many of which have already been successfully synthesized on a large scale\nvia chemical vapor deposition (CVD) or molecular beam epitaxy (MBE). This provides a\nfeasible route toward wafer-scale growth of CBPS.\nHowever, owing to the complex synthetic pathways of quaternary polar vdW materials\nand the multitude of physicochemical parameters, achieving wafer-scale CBPS samples\nrepresents a worldwide challenge. Addressing this issue calls for collective efforts from the\nglobal materials research community and is beyond the scope of the present work. We hope that\nthe reviewer can understand these issues and challenges."}, {"round": 2, "reviewer_comments": {"reviewer_1": "After careful reading of the revised version, the authors have demonstrated that they have successfully addressed the initial\nconcerns. I have no further comments.", "reviewer_2": "The authors have thoroughly addressed all of my review comments.", "reviewer_3": "The authors had put sincere efforts to address comments raised by this reviewer. Therefore, it can be accepted now."}, "author_response": ""}]

Decision letter