Massively parallel in-sensor skinomorphic computing

Domain: neuroscience
Published: 2026-04-08
Editorial outcome: minor_revision

Abstract
Real-time sensing and processing of a large amount of tactile information is essential for intelligent robotics and wearable technology. However, physical separation between sensors and processors in the traditional tactile sensing scheme makes these functionalities inaccessible, posing a major roadblock to the rapid advance of skinomorphic electronics. Here, we propose a massively parallel in-sensor skinomorphic computing scheme and demonstrate its promising applications in intelligent tactile perception. This scheme allows for achieving parallel sensing and processing of tactile information directly within sensor. We implement this proposed scheme by fabricating a 32×32 flexible capacitive pressure sensors array with excellent uniformity and endurance, and by cascading the sensors array with a memristive crossbar array. We experimentally demonstrate that the broken pressure patterns of the letter ‘NJU’ loaded on the sensors array can be sensed and restored in parallel, which is inaccessible with previously reported tactile technologies. Moreover, by networking the pressure sensors array with two memristive crossbar arrays, we show that textural features of the loaded complex pressure patterns can be directly extracted in a parallel manner and the tactile information can thus be compressed. Our work opens up an avenue for developing intelligent skins capable of real-time and high-throughput tactile perception.

Introduction
With the development of wearable technology and flexible edge intelligence1-9, there is a pressing need to realize parallel sensing and processing of tactile information, enabling real-time and high-throughput tactile perception10-13. Traditionally, tactile sensors and processors are physically separated14. The tactile data are sensed in a serial way and the sensed analog information is converted into digital data suitable for information processing and analyzing in a digital processor. In this sense, separate sensing and processing of a large amount of tactile data would lead to huge energy consumption and latency, which is undesirable for intelligent wearable technology and flexible edge artificial intelligence (AI)15-19. Therefore, a new computing paradigm is demanded for realizing energy-efficient and real-time tactile sensing and perception.

Bio-inspired in-sensor computing, as an alternative paradigm, can surmount the challenges associated with energy efficiency and latency in sensing and computing20-27. Previous findings in neuroscience suggest that the Merkel cell, one main type of tactile end organ in biological skin, is capable of sensing gentle touch and is essential for sophisticated sensory tasks including perception of edges and textures of pressure28-31. Similar to retina-inspired retinomorphic computing32-37, the development of skinomorphic computing inspired by Merkel cells may not only overcome the aforementioned challenges of traditional pressure sensors, but also provide a radically different avenue for the future development of intelligent wearable technology and intelligent flexible edge AI.

In this work, we propose a massively parallel in-sensor skinomorphic computing scheme inspired by Merkel cells of skin receptors for intelligent tactile perception. This proposed scheme is based on frequency division multiplexing (FDM) that employs continuous-time analog reading signals across multiple frequencies for simultaneously sensing and processing38-40. For hardware implementation of this proposed scheme, we network a 32×32 flexible capacitive pressure sensors array with a memristive crossbar array, and experimentally demonstrate one-shot sensing and processing of both loaded perfect and broken pressure patterns ‘NJU’. Furthermore, we show that connecting the capacitive sensor with two memristive crossbar arrays enables extraction of textural features from the loaded complex pressure patterns in a parallel manner. The large-area tactile information can thus be significantly compressed within the sensor. Our work provides an alternative tactile sensing paradigm desired for the future development of intelligent robots and wearable technologies.

Results
Massively parallel in-sensor skinomorphic computing

Fig. 1 schematically illustrates information processing in human skin and the skin-inspired skinomorphic in-sensor massively parallel computing. In human skin, Merkel discs and neurons play an important role in sensing and processing tactile information, respectively. The Merkel disc is a specialized tactile end organ formed by the synaptic connection between a Merkel cell and an Aβ-afferent ending41-44. As a key mechanoreceptor in the skin, the Merkel disc is responsible for sensing gentle touch and playing an essential role in complex sensory tasks such as perceiving edges and textures. Meanwhile, functioning as a slowly adapting type I (SAI) receptor, the Merkel discs encode mechanical pressure of spatial features such as edges and textures into bioelectrical signals. Subsequently, the encoded information is processed by the neurons in a massively parallel manner (Fig. 1a). It is such massively parallel sensing and processing capabilities of the skin that makes human being able to achieve real-time external pressure perception. Inspired by the capability of highly efficient information processing in human skin, we propose a massively parallel in-sensor skinomorphic computing paradigm based on a flexible capacitive pressure sensors array (Fig. 1b). Taking full advantage of the physical attributes of the capacitive crossbars, the pressure patterns (e.g., ‘NJU’) loaded on the sensors array can be sensed and processed simultaneously with high parallelism in the full analog domain.

The mechanism for parallel sensing and processing of the pressure pattern can be understood as follows. Firstly, the whole pressure pattern is applied to the capacitive sensors array. The pressure loaded on the capacitor in the i-th row and j-th column of the array alters the distance between its upper and lower electrodes, resulting in a corresponding change of its capacitance (ΔC ), as shown in the red box of Fig. 1b. The capacitance change in each capacitor of the array is directly related to its applied pressure, and thus the entire pressure pattern is sensed and converted into the capacitance change matrix (ΔC ) of the sensors array, in which m and n represent the number of rows and columns of the array, respectively. Next, we employ the frequency division multiplexing (FDM) approach to achieve one-shot readout and processing of the whole ΔC matrix, which represents the sensed pressure information. The FDM scheme takes advantage of data representation in a continuous-time analog signal and the physical attributes of the capacitive sensors array to process data in a continuous-time domain. By employing the capacitance law (i = Cdu/dt) and Kirchhoff’s current law, any individual sinusoidal voltage signals with different frequencies can be simultaneously fed into rows and processed by the sensors array. To achieve one-shot readout and processing, continuous-time signals by linear combinations of sinusoidal voltage signals are fed into rows of the capacitor array. Such a continuous-time signal can be transformed into a frequency spectrum with multiple peaks at different frequencies (Fig. 1c). These input continuous-time voltage signals of multiple frequency components can be used for reading and processing the sensed pressure information of the capacitor array to output the continuous-time current signals of multiple frequency components (Fig. 1d). The one-shot readout and processing of the whole pressure pattern loaded on the sensors array are accomplished while these continuous-time signals flow through the sensors array.

The operations of readout and processing of a pressure pattern can be described by the matrix-matrix multiplication (MMM), that is a weight matrix (V ) multiplies the pressure pattern matrix (ΔC ): V ·ΔC , in which k denotes the number of frequencies of the signal input into each row. The readout or processing mode is performed depending on whether V is an identity matrix. This MMM can be implemented by using the continuous-time FDM scheme based on the capacitive sensors array. The matrix V is represented by m voltage signals applied simultaneously to the m rows of the capacitive crossbar array. The voltage signal of the i-th row consists of k frequency components with different amplitudes (Vk) (Fig. 1c). The full set of voltage signals (Vk,Vk,…,Vk) forms the matrix V . Following the capacitance law (Cu = ∫ 𝑖d𝑡) and Kirchhoff’s current law, the integration of the output current from the j-th column over a time segment (0-T) also consists of k frequency components (Fig. 1d). As for the k-th frequency from the j-th column, it can be expressed by the equation in the dashed box between Fig. 1c and Fig. 1d. By varying all the k frequencies and n columns in this equation, the resulting k×n equations constitute the MMM: V ·ΔC = I . The processed result that denoted as matrix I is represented by the k frequency components with different amplitudes from the integrations of the output current signals in each column, that is I = (∫ 0 T I 1 k𝑑𝑡,∫ 0 T I 2 k𝑑𝑡,… ,∫ 0 T I n k𝑑𝑡). In particular, if V is an identity matrix, the parallel processing degrades into parallel sensing and readout. Our scheme achieves one-shot sensing and processing of the pressure pattern using the FDM approach. In contrast, the conventional tactile sensors array performs pressure pattern readout sequentially from each sensor, followed by one-by-one analog-to-digital conversion and then sends it to the backend for processing. This results in significant latency and power consumption, especially for large-area and high-throughput tactile perception tasks. Note that for an N×N pixels array, our scheme enables parallel readout for all pixels in the array by common row lines and column lines with only 2N ports. Whereas, conventional architecture requires individual wiring of each pixel and needs N2 ports to achieve simultaneous readout of all pixels in parallel. Our scheme significantly reduces the complexity of wiring and layout for massively parallel computing compared with conventional architecture. This advantage in scalability is much more pronounced when the array size becomes larger.

Flexible capacitive pressure sensors array

For the hardware implementation of the skinomorphic in-sensor parallel computing scheme, we fabricated a flexible 32×32 capacitive sensors array with wide-range, robust, uniform and fast pressure-sensing capabilities (Fig. 2). The structure of each capacitive sensor in the array consists of a polyimide (PI) substrate, a gold (Au) bottom electrode, a polydimethylsiloxane (PDMS) dielectric layer with columnar microstructure, a poly (3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT: PSS) top electrode and a top PI packaging layer (Fig. 2a). The morphology of the PDMS dielectric layer with its well-defined columnar microstructure is shown in Fig. 2b. The columnar microstructures have a width of 10 μm, a height of 30 μm, and a periodicity spacing of 30 μm. The large-scale periodicity and well-defined columnar microstructure enable robust and uniform pressure-sensing performance across the tactile capacitive sensors array. To achieve better flexibility of the sensors array and facilitate adhesion between the electrode and the PDMS layer, the conductive polymer PEDOT: PSS is used as the electrode material. A detailed fabrication process is provided in the Methods and Supplementary Fig. S1.

We conducted pressure response measurements of the flexible capacitive sensors array for evaluating its response range, uniformity, endurance and speed. The relationship between capacitance change and the applied pressure is fundamental for pressure pattern sensing and processing. We measured the sensor’s capacitance change (ΔC = C - C ) under different applied pressures (Fig. 2c) (C is the capacitance under pressure load and C is the baseline capacitance without pressure). The correlation is nearly linear at low pressure loads, as shown in the inset of Fig. 2c. The effective pressure response range spans from 0.155 kPa to 32.875 kPa, enabling the sensors array to effectively detect both light and heavy pressure loads. We also measured the sensor by applying alternating-current (AC) voltages with different frequencies under periodic pressure loading (Supplementary Fig. S2). The sensor can successfully sense the loaded pressure and convert the signal into identical capacitance changes over a broad frequency range from 0-5 MHz.

Moreover, uniformity and robustness are also critical for in-sensor parallel computing implementation and real-world applications. To assess uniformity, we measured the capacitance responsivity (ΔC/C ) of the 1024 devices within the capacitive sensors array and the statistical experimental results present high uniformity across the sensors array (Fig. 2d). In terms of robustness, we tested the endurance of the pressure sensor by repeatedly applying and removing pressure over an extended period. The results reveal that there is no sign of degradation within the measurement window (4000 s) (Fig. 2e), demonstrating excellent long-term durability. More characterization tests of the sensor parameters such as response time, linearity, sensitivity, detection limit and hysteresis during loading and unloading cycles are displayed in Supplementary Fig. S3-S6. These results highlight the excellent performance of the fabricated flexible capacitive sensors array, providing a reliable hardware platform for the implementation of skinomorphic in-sensor parallel sensing and processing.

Hardware implementation of skinomorphic in-sensor parallel computing

We experimentally implemented the skinomorphic in-sensor parallel sensing and processing with the capacitive crossbar sensors array (Fig. 3). Fig. 3a schematically shows the physical implementation of the MMM in experiments. The conductance values of the memristors in k×m one-transistor-one-memristor (1T1R) array are configured for representing the weight matrix V . Continuous-time sinusoidal voltage signals with k frequencies and a unified amplitude are simultaneously input into the m rows of the 1T1R array as carrier waves. The voltage signal with frequency f is applied to the i-th row of the 1T1R array. The amplitudes of these k frequencies from the m output current signals correspond to the conductance values of the 1T1R array, that is the matrix V . These currents are then converted into voltages as matrix signals and fed into the m rows of the capacitive array for multiplication with the ΔC matrix. To implement this scheme, we connected the 32×32 flexible capacitive pressure sensors array with a 32×32 1T1R array, along with an automatic pressure loading setup and the peripheral circuits (Fig. 3b). More detailed performance data for the memristor and their array including uniformity and frequency characteristics are shown in Supplementary Fig. S7-S12. In conjunction with the frequency characteristics of the capacitive pressure sensor (Supplementary Fig. S2), our integrated system can be operated over a wide frequency range from 0-5 MHz, which sufficiently fulfills the requirement of practical deployment in hardware. We employed a 3D-printed relief Teflon pattern with the letters ‘NJU’ (inset of Fig. 3b) and pressed it onto the sensors array through the automatic pressure loading setup. More details about readout circuits and operations are provided in the Methods and Supplementary Fig. S13. By programming the conductance of the 1T1R array, which represents the weight matrix V , we demonstrated two distinct operations of skinomorphic in-sensor parallel computing: parallel sensing and parallel processing.

We performed in-sensor parallel sensing by configuring the conductance of the 1T1R array into an identity matrix. In this condition, the voltage matrix signals that are fed into the capacitive sensors array contain only one distinct frequency per row, all with a unified amplitude. We conducted parallel sensing by using a perfect ‘NJU’ pressure pattern loaded on the sensor array. The output signals from the capacitive sensors array indicate that the ‘NJU’ pattern can be accurately captured (Fig. 3c). The experimental results are consistent with the simulation in the frequency spectrum (Fig. 3d and Supplementary Fig. S14), further validating the accuracy and reliability of the parallel sensing and readout process. Moreover, the defective ‘NJU’ pattern loaded on the sensors array (Fig. 3e) can be restored and fixed by configuring the 1T1R array with a specific restoration weight matrix (Fig. 3f). In this case, the voltage matrix signals applied to each row of the capacitive sensors array contain k frequency components, with amplitudes corresponding to the restoration weight matrix. As a result, the broken loading pattern can be successfully reconstructed (Fig. 3g). Note that the restoration matrices for the two similar defective patterns are also quite identical, illustrating that only a small portion of the weights needs to be updated instead of rewriting all weights of the memristor array for a new pressure pattern, as shown in Supplementary Fig. S15. The real-time training of memristor arrays is also feasible and practical, as the on-chip learning of memristor arrays has been demonstrated in the previous work45.

Comparisons of our work with recently reported state-of-the-art tactile sensory systems are displayed in Supplementary Note Ⅰ. The in-sensor parallel restoration of a defective pressure pattern highlights the processing capability of our proof-of-concept skinomorphic sensory system, which is inaccessible with previously reported flexible strain or pressure sensors.

Parallel textural feature extraction of complex pressure patterns

The proposed in-sensor skinomorphic computing scheme can be further used for perceiving complex pressure patterns in the frequency domain while simultaneously compressing the sensory data. The parallel MMM implemented by the capacitive sensors array enables textural feature extraction and tactile information compression directly within the front-end sensor for large-area complex pressure patterns, which typically contain lots of redundant information, e.g., periodic spatial structure. In theory, the textural feature of a pressure pattern can be extracted through a two-dimensional Discrete Fourier Transform that involves a three-matrix multiplication: T = G ‧C‧G , where C represents the capacitance matrix reflecting the input loading pressure pattern, G and G are complex transform matrices. T is the final output complex matrix representing the spatial frequency spectrum (textural feature) of the input pressure pattern. As for a squaral capacitive sensors array, C is also a square matrix and thus G = G = A + Bi, where A and B are real matrices and i is the imaginary unit. In this condition, the multiplication can be written as: T = (A + Bi)‧C‧(A + Bi) = (ACA – BCB) + i(ACB + BCA). Since the real part of the textural feature T of the pressure pattern is only considered, T = ACA – BCB can be defined, in which

A = [cos(π 0∙0 / 16) cos(π 0∙1 / 16) ⋯ cos(π 0∙31 / 16); cos(π 1∙0 / 16) cos(π 1∙1 / 16) ⋯ cos(π 1∙31 / 16); ⋮ ⋮ ⋱ ⋮; cos(π 31∙0 / 16) cos(π 31∙1 / 16) ⋯ cos(π 31∙31 / 16)];

B = [sin(π 0∙0 / 16) sin(π 0∙1 / 16) ⋯ sin(π 0∙31 / 16); sin(π 1∙0 / 16) sin(π 1∙1 / 16) ⋯ sin(π 1∙31 / 16); ⋮ ⋮ ⋱ ⋮; sin(π 31∙0 / 16) sin(π 31∙1 / 16) ⋯ sin(π 31∙31 / 16)].

This operation of matrix multiplication can be completed with high parallelism by using a similar mechanism described in Fig. 3. To implement this task, we networked the capacitive sensors array with two 1T1R memristive arrays, as schematically displayed in Fig. 4a. The photograph of the whole integrated system including tactile sensors array and memristors arrays is shown in Supplementary Fig. S16. Firstly, the pressure pattern is loaded onto the capacitive sensors array, and the conductance values of both two memristive arrays are configured proportionally to matrix A. Carrier waves with frequencies f , f , …, f are applied to the rows of the left memristive array for matrix readout and multiplication. The frequency spectrum of the output signals from the columns of the right memristive array represents the multiplication result of ACA, which is then stored in a buffer memory. Next, the conductance values of both memristive arrays are reconfigured to represent the matrix B. The multiplication of BCB is performed in the same manner under the continuous loading of the pressure pattern on the sensors array, yielding the final result of the textural feature T of the pressure pattern.

We demonstrated two types of textural feature extraction for both periodic pressure patterns and non-periodic pressure patterns, with the corresponding results shown in Fig. 4b and Fig. 4c, respectively. We first performed textural feature extraction for periodic pressure patterns (Fig. 4b-i and 4b-iii). The output textural feature consists of discrete points on the frequency plane (Fig. 4b-ii and 4b-iv). The sparsity of the output spatial frequency spectrum indicates that the tactile information has been significantly compressed compared with the original input pressure pattern. Notably, the spacing between the points on the frequency plane increases as the spatial period of the original pressure pattern decreases, which is consistent with the principle of the two-dimensional Fourier transform. We next conducted textural feature extraction for non-periodic pressure patterns (Fig. 4c-i and 4c-iii). The output textural feature appears quasi-continuous on the frequency plane (Fig. 4c-ii and 4c-iv). As the pressure-loading area decreases, the region of non-zero frequency components expands, which also aligns with the Fourier transform theory. These results validate the feasibility of implementing textural feature extraction based on our capacitive sensors array with high parallelism. Note that the extracted textural features are significantly sparser than the original pressure patterns, enabling accurate tactile pattern recognition with reduced data volume, which is significant for deployed edge intelligent agents.

Furthermore, we have performed the recognition task based on the dataset comprising both periodic patterns classes and nonperiodic patterns classes. The original pressure patterns and the corresponding spatial-frequency patterns after two-dimensional Fourier transform are displayed in Fig. 4d and 4e, respectively. Neural network recognition details are provided in the Method section. According to the results shown in Fig. 4f, the recognition accuracy based on spatial-frequency information is comparable to that of original images, which demonstrates the robustness and effectiveness of patterns recognition based on spatial-frequency information.

Methods
Fabrication of the PDMS dielectric layer with columnar microstructure

Preparation of the PDMS microstructure dielectric layer was carried out after the hydrophobic treatment of the Si-mould (Supplementary Fig. S1)46,47. The Si-mould was patterned using photolithography, and then the photoresist was used as a mask for plasma etching by inductively coupled plasma (ICP). The result, as shown in Supplementary Fig. S1a, shows that the columnar microstructures were fabricated with a length and width of 10 μm, a depth of 30 μm, and a periodicity of spacing of 30 μm. A 1:10 (wt%) mixture of curing agent to PDMS elastomer (Sylgard 184, Dow Corning) was prepared and stirred for 1 hour. The prepared solution was then placed in a vacuum for 4 hours to remove bubbles. The solution was then spin-coated onto the Si-mould at a speed of 500 rpm for 40 seconds. Subsequently, the PDMS with the Si-mould was baked at 90 ℃ for 4 hours, and then peeled off from the Si-mould by thermal release tape (TRT) (Supplementary Fig. S1d). Moulded PDMS films were then transferred onto a flexible PI substrate printed with strip-shaped bottom electrodes.

Fabrication of the flexible capacitive pressure sensors and memristors array

The sensors array fabrication: The flexible capacitive pressure sensors were composed of a typical sandwich structure. A flexible 32×32 capacitive pressure sensors array was fabricated using crossbar structure with 32 conductive PEDOT:PSS top electrode strips (~2.0 mm wide) for flexible electrodes, a moulded PDMS film with a microstructure as the dielectric layer, and 32 bottom electrode strips (~2.0 mm wide, Cu/Au) preprinted on the PI substrate. Finally, the flexible sensor was encapsulated by the PI tape.

The memristors array fabrication: The memristive devices were fabricated according to our previous report39. The device has a typical sandwich structure (Pd/Ta/HfO /Pd) that consists of the bottom/top electrodes layer Pd (50 nm thick), the HfO switching layer (6 nm thick), and the Ta capping layer (80 nm thick). All the above layers were patterned by utilizing standard photolithography, etching and deposition processes. The feature line width of the device is 2 μm.

Characterization of the flexible capacitive pressure sensors and memristors array

The sensors array characterization: Morphologies of the moulded PDMS film with columnar microstructures were investigated by a field-emission scanning electron microscope (FESEM, FEI Company). The capacitance of the sensors array was measured using an Agilent B1500A semiconductor characterization system (5 kHz frequency, 2 V bias). Pressure was loaded through employing various 3D-printing pattern molds onto the flexible sensors. Cyclic loading tests of the flexible sensors with different increasing pressure levels were performed at 0.155 kPa to 10.0 kPa.

The memristors array characterization: The electrical characterization of the memristive devices was measured by using Agilent B1500A semiconductor characterization system, including measurements of basic electrical properties such as uniformity, stability, multi-resistance states, and frequency characteristics. The multi-resistance states of the memristor determine the number of weights that can be used for computing. These results are shown in the Supplementary Fig. S7-S12.

Operations of the pressure sensors array and memristors array

To reliably realize the conductance configuration of memristor crossbar array, we gradually programmed the conductance values of the memristive devices by applying control signals by an MCU and programming voltage pulses through an Agilent B1530 pulse generator, where the control signals are used for selecting the individual memristors by switching the transistor and the voltage pulses are employed for tuning memristor conductance gradually. Accordingly, we programmed the memristor conductance into a defined range using a closed-loop writing method implemented with a script in LabVIEW: if G < (G – ∆G), a positive pulse would be applied on the memristive device; else if G > (G + ∆G), a negative pulse would be applied. The deviations of the programmed conductance values are controlled within about 5.0 μS for each conductance level. Note that the conductance variation can be further reduced by reducing appropriate initial parameters for ∆G used in the closed-loop writing method.

In the hardware implementation, continuous-time voltage signals with identical amplitude and different frequencies are generated by the voltage-controlled oscillators. These carrier wave signals are simultaneously fed into the rows of the memristor crossbar array in parallel. The output signals of the memristor array are then fed into the rows of the capacitive sensor array for multiplication through the amplifying circuit. To obtain the frequency components from the output signals of the sensor array, we employ a digital oscilloscope equipped with a Fourier transform function, which can clearly distinguish different frequencies and read them out.

The amplitudes and frequencies of the input signals are chosen based on the experimental equipment and memristive hardware, so that the parallel matrix multiplication in the frequency domain can implemented in a reliable way. In this way, the amplitudes of the input voltage signals are constrained below the half of the memristor’s switching threshold voltage to ensure the precision and accuracy. In experiments, an amplitude of 0.1 V was employed for the first memristor crossbar array. The amplitude applied to the second memristor crossbar array is determined by both the actual pressure pattern and the capacitance parameter of the amplifying circuit. We selected the capacitance value of the amplifying circuit comparable to that of the flexible capacitive pressure sensor. Thereby, the resulting input voltages amplitudes of the second memristor array also maintain below half of the memristor’s switching threshold voltage, avoiding the unintended resistance-state switching.

As for the choice of frequency parameter, the highest frequency component is selected to remain below the analog bandwidth limit of the overall system. Previous studies have demonstrated that memristors can exhibit bandwidths exceeding tens of gigahertz48. Thus, the practical bandwidth limitation arises from the amplifying circuit rather than the memristor devices in our system. The operational amplifier used in our setup (OPA891, Texas Instruments) provides a gain–bandwidth product of approximately 180 MHz, allowing us to flexibly select carrier frequencies well within this range for reliable frequency-division multiplexing operation.

Implementation of massively parallel readout in flexible pressure sensors array

Signal generators, trans-impedance amplifiers (TIAs) and an oscilloscope with a built-in frequency analyzer were employed to demonstrate massively parallel sensing and processing in the flexible 32×32 capacitive pressure sensors array. Signal generators with 32 channels were used as various frequency signal sources. TIAs were used to convert the current signals into voltage signals for measurement. Frequency analyzer was performed to measure the output results from the flexible pressure sensors array. In sensing mode, the individual-frequency sinusoidal voltage signals were input as carrier signals into each row of the flexible pressure sensors array. In processing mode, the above-mentioned frequency signals were input into different rows of the memristive devices array with different weights (conductance). The output current carrying weighted information was converted into a voltage before being fed into the flexible capacitive pressure sensors array. The output signals of the flexible capacitive pressure sensors array in both modes mentioned above were measured by the oscilloscope with a frequency analyzer.

Simulations of the two-dimensional Fourier transform and pattern recogntion

The textural feature extraction through two-dimensional discrete Fourier transform in Fig. 4 is to basically carry out three-matrix multiplications: T = ACA - BCB. The above matrix multiplications are performed by Python NumPy toolbox and the resulting matrix T is displayed through Python Matplotlib toolbox. The simulation parameters of the matrix A and B are set according to the matrix’s values shown in the main text. The simulation parameters of the matrix C are set by the pressure pattern with values of each element normalized into the scope of 0-1. Note that the operational principles in hardware implementation of the above three-matrix multiplications is the same as the two-matrix multiplication, which has already been demonstrated in experiments as shown in Fig. 3.

To better demonstrate the potential and usability in real applications, the downstream pattern recognition process following the textural feature extraction is performed. The dataset comprises five classes including periodic patterns stripes and grids, and nonperiodic patterns circles, rectangles, and triangles. Each class has 200 different binarized images for training and 40 different binarized images for testing. The recognition is based on the original images and the transformed spatial-frequency information of original images after two-dimensional discrete Fourier transform, respectively. The recognition is performed by a simple two-layer neural network, and the training and testing processes are achieved through PyTorch.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this study, the authors propose a skinomorphic in-sensor massively parallel computing scheme and demonstrate its potential applications. The proposed system is designed to sense a broken letter pattern (\"NJU\") through a restoration process using memristor arrays and presents a conceptual framework for detecting textural features with data compression. However, the innovation points and advantages of the proposed system are not clearly demonstrated. Moreover, the results provided are insufficient to support the authors’ claims or help readers fully understand the underlying mechanisms. Therefore, I cannot recommend this manuscript for publication in Nature Communications due to concerns regarding its novelty and overall contribution to the field.\n1) The manuscript does not appear to address any fundamental challenges or present groundbreaking advancements. Although microstructured sensors are introduced as a key component, their performance benefits are not clearly shown. The characterization tests are rather conventional, and the system’s performance does not outperform existing technologies. In addition, sensor parameters such as sensitivity, linearity, response time, detection limit, and hysteresis during loading and unloading cycles should be analyzed in more detail.\n2) The memristor structure employed is highly conventional. Furthermore, its rigid form factor appears unsuitable for skinomorphic in-sensor massively parallel computing schemes.\n3) The authors should provide detailed performance data for the memristors and their arrays, including I–V characteristics, stability, multi-level retention, pulse operation behavior, reliability, and uniformity.\n4) High-resolution images of the devices—particularly close-up views of the tactile sensors, memristor arrays, and the fully integrated system—should be included to facilitate better understanding and visualization.\n5) Clarification is needed regarding the training process of the memristor arrays in Figure 3. It appears that the restoration of broken letter patterns may not be practical, as real-time training of memristor arrays for multiple pattern types is highly challenging.\n6) The authors should clearly explain how the amplitude and frequency parameters were determined for each memristor array. The current discussion is unclear and lacks sufficient depth.\n7) It appears that the system operations depicted in Figure 4 are based solely on simulations. If so, the authors should detail how the simulation parameters were set. If the results are experimental, the integration process for each system component should be clearly described. In either case, comprehensive measurement results and a component-by-component analysis are required.\n8) The proposed parallel textural feature extraction of complex pressure patterns in Figure 4 does not seem practically useful without further downstream information processing. The authors should demonstrate more realistic and practical applications beyond mere extraction of textural features.", "reviewer_2": "The work by Li et al. entitled “Skinomorphic in-sensor massively parallel computing” (Manuscript ID: NCOMMS-25-42827-T) reports a Merkel cell inspired parallel computing paradigm for intelligent tactile perception. The scheme employs continuous-time analog reading signals across multiple frequencies for simultaneously sensing and processing based on frequency division multiplexing (FDM). The authors have demonstrated one-shot sensing and processing of both perfect and broken pressure patterns ‘NJU’ by using 32×32 flexible capacitive pressure sensors array networked with a memristive crossbar array. Furthermore, textural features from the complex loaded pressure patterns can also be effectively extracted in a parallel manner by connecting the capacitive sensors array with two memristive crossbar array. The work provides a universal and advanced tactile sensing scheme for real-time and high-throughput tactile perception over previous technologies. Overall, the work is novel and the experimental data can support the conclusions of the article. The work is also very interesting and inspiring to me. Additionally, the paper is well-organized and well written. Therefore, I strongly recommend it for publication in the prestigious journal Nature Communications, after addressing the following minor questions.\n(1) Inspired by the mechanism and function of biological skin, the authors have proposed an innovative tactile in-sensor computing paradigm with high parallelism. The word “skinomorphic” appropriately describes the unique feature of the proposed scheme. The discussion about structure and function of the biological skin especially Merkel cell that acts as one main type of tactile end organ cell, is relatively little in the manuscript. The authors are suggested to supplement more elaborations regarding the detailed mechanism and functionality of the Merkel cell and the human skin.\n(2) The hardware implementation of skinomorphic in-sensor parallel computing is based on a capacitive sensors array networked with a memristor array. The descriptions of how to operate the one-transistor-one-memristor (1T1R) array is brief in the manuscript. The authors are suggested to supplement more details on memristor array operations such as the conductance configuration in the Method section.\n(3) The memristor array is responsible for representing the weight matrix Vkm that multiplies with the capacitance matrix in the hardware implementation. The experimental details of how continuous-time voltage signals with multiple frequencies are fed into the memristor array and the readout method need to be further elaborated in the Method section, so that the importance of using the memristor can be highlighted.\n(4) The authors have experimentally demonstrated the textural feature extraction for both periodic pressure patterns and non- periodic pressure patterns in Figure 4, which has verified the capability and feasibility of the proposed scheme. The authors are also encouraged to further perform the recognition/classification of the complex pressure patterns based on the extracted textural features and make a comparison with that directly based on the original pressure patterns.\n(5) Some minor corrections: The units and color bars of Figure 4b and 4c are missing and the authors need to add them in.", "reviewer_3": "In this work, Li et al. present a novel in-sensor computing paradigm for massively parallel tactile perception, which is inspired by the Merkle cells of the human skin. The paradigm utilizes the continuous-time data representation and frequency division multiplexing to realize parallel reading and processing of pressure pattern, overcoming the challenge of traditional tactile sensing schemes. The authors fabricated a 32×32 flexible capacitive pressure sensors array and networked it with a memristive crossbar array, and experimentally demonstrated one-shot sensing and restoration of the broken pressure patterns ‘NJU’. Furthermore, they demonstrated textural features of some typical complex pressure patterns by using two memristor array and the capacitive pressure sensors array. The study offers an in-sensor parallel computing scheme for highly efficient large-scale tactile information perception in real-time, revolutionizing the traditional tactile sensing and perception. This work is timely and significant to the field of intelligent tactile sensing. Therefore, I would recommend it for publication after minor revisions.\n(1) The parallel readout and processing of broken pressure pattern as shown in Fig. 3f is really interesting. I note that the units of the weights for restoration in Figure 3f are voltage, but the units of the weights for memristors array are typically conductance. Please provide relevant explanations on reason why the voltage unit is used for the weights.\n(2) The textural feature extraction based on in-sensor parallel Fourier transform in Figure 4 is interesting to me. Particularly, the textural feature extraction enables robust recognition of the pressure pattern under compressed data volume. Such advantage can be further highlighted by providing the comparison of recognition accuracy between the processed pattern based on the authors’ approach and original pressure patterns.\n(3) The statistical data of Figure 2d illustrates the good uniformity of the fabricated capacitive sensors array. To quantify the uniformity, the authors are suggested to calculate the mean value and the standard deviation."}, "author_response": "1 Response to Referees’ Comments\n2\n4\n6 computing scheme and demonstrate its potential applications. The proposed system is\n7 designed to sense a broken letter pattern (\"NJU\") through a restoration process using\n8 memristor arrays and presents a conceptual framework for detecting textural features\n9 with data compression.\n11 demonstrated. Moreover, the results provided are insufficient to support the authors’\n12 claims or help readers fully understand the underlying mechanisms. Therefore, I cannot\n13 recommend this manuscript for publication in Nature Communications due to concerns\n14 regarding its novelty and overall contribution to the field.\n16 we also appreciate the constructive suggestions for further improving our manuscript.\n18 proposed computing scheme: 1) The concept of in-sensor skinomorphic computing is\n19 novel, which enables one-shot readout and pre-processing for all pixels of the array\n20 with highly parallelism in the frequency domain, leading to ultralow latency and\n21 ultrahigh throughput. The proposed computing scheme is in sharp contrast with the\n22 conventional tactile sensory system, which signals from each pixel or each row are read\n23 out sequentially and processed in the time domain, resulting in ultrahigh latency\n24 especially for large-scale sensor array. 2) Our proposed proof-of-concept in-sensor\n25 skinomorphic computing is technologically advanced. As for an N×N pixels array, our\n26 scheme enables parallel readout for all pixels in the array by common row lines and\n27 column lines with only 2N ports. Whereas, conventional architecture requires\n28 individual wiring of each pixel and needs N2 ports to achieve simultaneous readout of\n29 all pixels in parallel. Our scheme significantly reduces the complexity of wiring and\n1\n30 layout for massively parallel computing compared with conventional architecture. This\n31 advantage in scalability is much more pronounced when the array size becomes larger.\n33 underlying operational mechanisms, we have performed comprehensive\n34 characterizations of the tactile sensors and memristors in experiments, as well as\n35 simulations in downstream practical applications beyond the textural feature extraction.\n37 provided in the revised manuscript by following the reviewer’s suggestions.\n39\n40 (1) The manuscript does not appear to address any fundamental challenges or present\n41 groundbreaking advancements. Although microstructured sensors are introduced as a\n42 key component, their performance benefits are not clearly shown. The characterization\n43 tests are rather conventional, and the system’s performance does not outperform\n44 existing technologies. In addition, sensor parameters such as sensitivity, linearity,\n45 response time, detection limit, and hysteresis during loading and unloading cycles\n46 should be analyzed in more detail.\n48 innovation of our work is the first proposal of an in-sensor skinomorphic computing\n49 scheme, which is a fundamental breakthrough in the basic signal processing paradigm\n50 of the sensor system. The data representation and processing of our scheme are\n51 accomplished in the frequency domain through continuous-time carrier waves. All\n52 pixels of the whole sensor array can thus be simultaneously read out and pre-processed\n53 in parallel by using common row lines and column lines, which is significant in\n54 reducing latency and wiring complexity for large-scale sensor array. Our scheme\n55 essentially revolutionizes conventional pixel-by-pixel time-serial signal processing\n56 paradigm in the time domain that leads to severe latency especially for large-scale\n57 pressure patterns. To achieve parallel readout and processing of all pixels in the array\n58 concurrently, traditional time-domain approach requires individual wiring for each\n2\n59 pixel, which results in extremely complex layout and is unfeasible for large-scale\n60 flexible tactile sensor array. Our frequency-domain multiplexing scheme resolves the\n61 fundamental challenges of conventional time-serial paradigm in terms of latency,\n62 throughput, and scalability.\n64 in-sensor skinomorphic computing scheme, rather than pursuing the extreme\n65 performance of the single pressure sensor. Therefore, we used a typical flexible pressure\n66 sensor with mature material and structure to implement the proposed concept in\n67 experiments, and the pressure sensor itself is not the key component and innovation of\n68 our work. Despite this, following the reviewer’s suggestions, we have also performed\n69 additional comprehensive characterizations on the pressure sensor parameters. The\n70 experimental measurements on the hysteresis during loading and unloading cycles are\n71 shown in Fig. R1. The hysteresis is 1.87% during 3 cycles of loading and unloading of\n72 the capacitive flexible sensor. Moreover, the response time is 200 ms and the recovery\n73 time is 400 ms when loading pressure is approximately 4.5 kPa, as shown in Fig. R2.\n75 the sensor’s sensitivity, linearity, and detection limit as displayed in Fig. R3. The\n76 sensitivities of the two pressure ranges are S = 0.13 pF·kPa-1 and S = 0.044 pF·kPa-1.\n1 2\n1\n78 y =0.044x+0.951 and the extracted linearities are R2=0.997, R2=0.983 respectively. The\n2\n79 detection limit (LOD) is 0.32 kPa according to the formula: LOD = 3σ/S, where σ is\n80 the standard deviation and S is sensitivity. These performance parameters of the tactile\n81 sensor can be further improved in the future by optimizing the structure and fabrication\n82 process.\n3\n83\n85 flexible pressure sensor. The hysteresis is 1.87% during 3 cycles of loading and\n86 unloading of the capacitive flexible sensor.\n87\n88\n90 time is 200 ms and the recovery time is 400 ms when loading pressure is approximately\n91 4.5 kPa.\n92\n4\n93\n95 sensitivities of the two pressure ranges are S = 0.13 pF·kPa-1 and S = 0.044 pF·kPa-1.\n1 2\n1\n97 y =0.044x+0.951 and the extracted linearities are R2=0.997, R2=0.983 respectively. The\n2\n98 detection limit (LOD) is 0.32 kPa that is calculated according to the formula: LOD =\n99 3σ/S, where σ is the standard deviation and S is sensitivity.\n100\n102 supplementary information as Supplementary Fig. S3-S5. For ease of reviewing, the\n103 revision has been provided below, which is highlighted in yellow.\n104\n105 “…More characterization tests of the sensor parameters such as response time, linearity,\n106 sensitivity, detection limit and hysteresis during loading and unloading cycles are\n107 displayed in Supplementary Fig. S2-S5.”\n108 [Page 9, Lines 200-202]\n109\n110 (2) The memristor structure employed is highly conventional. Furthermore, its rigid\n111 form factor appears unsuitable for skinomorphic in-sensor massively parallel\n112 computing schemes.\n5\n114 as a peripheral circuit of the pressure sensor array for implementing the proposed in-\n115 sensor skinomorphic computing scheme. To that end, we used memristor array to load\n116 the matrix weights on the frequency spectrum of the carrier waves for multiplication\n117 with the pressure pattern matrix of the sensor array. Although the current memristor\n118 array is rigid, the structure is broadly applied and the functionality is sufficient for\n119 experimental demonstration of our proposed concept, as shown in Fig. 3 of the main\n120 manuscript. Moreover, it is also technologically feasible for fabricating flexible\n121 memristor array by integrating the memristor array onto the flexible substrate, as\n122 demonstrated in [Nature Communications 16, 4312 (2025)], [Nature\n124 will commit on integrating memristor arrays and sensor array onto one flexible\n125 substrate as an intelligent electronic skin for practical applications.\n126\n127 (3) The authors should provide detailed performance data for the memristors and their\n128 arrays, including I–V characteristics, stability, multi-level retention, pulse operation\n129 behavior, reliability, and uniformity.\n131 suggestion, we have provided detailed performance data for the memristor and the array,\n132 including I–V characteristics (Fig. R4a), stability (Fig. R4b), multi-level retention (Fig.\n135 are suitable for demonstration and hardware implementation of the proposed in-sensor\n136 skinomorphic computing concept.\n6\n137\n139 characteristics of our memristor device over 1000 bipolar direct current (DC) cycles. b,\n141\n142\n144 behavior, and bending stability. a, Retention characteristics of six programmable\n145 conductance states over 10⁴ seconds. b, Long-term potentiation and depression\n146 (LTP/LTD) characteristics under pulse modulation. c, Reliability performance of our\n147 memristor on a flexible substrate for bending test of 1500 mechanical bending cycles.\n149 bent the substrate by reducing the distance between its two ends to half of the original\n150 length.\n151\n7\n152\n154 switching characteristics obtained from 32 memristors integrated in a flexible array. b,\n156\n158 supplementary information as Supplementary Fig. S7-S9. For ease of reviewing, the\n159 revision has been provided below, which is highlighted in yellow.\n160\n161 “…More detailed performance data for the memristors and their array are shown in\n163 [Page 10, Lines 235-236]\n164\n165 (4) High-resolution images of the devices—particularly close-up views of the tactile\n166 sensors, memristor arrays, and the fully integrated system—should be included to\n167 facilitate better understanding and visualization.\n169 reviewer’s suggestion, we have provided the images of each component and the whole\n170 integrated system to facilitate visualization and understanding, as shown in Fig. R7.\n8\n171\n173 32×32 flexible capacitive pressure sensors array networked with two 32×32 one-\n174 transistor-one-memristor (1T1R) array.\n175\n177 supplementary information as Supplementary Fig. S13. For ease of reviewing, the\n178 revision has been provided below, which is highlighted in yellow.\n179\n180 “…The photograph of the whole integrated system including tactile sensors array and\n181 memristors arrays is shown in Supplementary Fig. S13.”\n182 [Pages 13-14, Lines 305-306]\n183\n184 (5) Clarification is needed regarding the training process of the memristor arrays in\n186 as real-time training of memristor arrays for multiple pattern types is highly challenging.\n188 for the two similar defective patterns would be also quite identical, indicating that only\n9\n189 a small portion of the weights is required to be updated instead of rewriting all weights\n190 of the memristor array for a new pressure pattern. To demonstrate this, we have\n191 performed additional simulations based on two similar defective letter patterns, as\n192 shown in Fig. R8. The two defective patterns with random background noise can be\n193 successfully restored by using the corresponding restoration matrix. According to the\n194 results, the restoration matrices for the two similar defective letter patterns are almost\n195 identical. Moreover, the defective pattern can also be restored by using the other\n196 restoration matrix, as displayed in the dashed rectangle in Fig. R8. Although there are\n197 still some defects, the restored patterns can also be accurately recognized by the neural\n198 network. We would also like to clarify that the real-time training of memristor arrays is\n199 feasible and practical, since prior work [Science 381,1205 (2023)] has already\n200 demonstrated the on-chip training of memristor arrays. Therefore, it is technologically\n201 feasible to achieve real-time restoration and recognition for multiple broken letter\n202 patterns.\n203\n205 defective letter pattern_1 and _2 with random background noise can be restored through\n206 the corresponding restoration matrix_1 and _2, respectively. Moreover, the defective\n207 pattern_1 can also be fixed by restoration matrix_2 with no background noise as shown\n208 in the upper half of the dashed box. The defective pattern_2 can also be fixed by\n10\n209 restoration matrix_1 with no background noise as shown in the lower half within the\n210 dashed box.\n211\n213 supplementary information as Supplementary Fig. S12, as well as additional\n214 discussions into the revised manuscript. For ease of reviewing, the revision has been\n215 provided below, which is highlighted in yellow.\n216\n217 “…Note that the restoration matrices for the two similar defective patterns are also quite\n218 identical, illustrating that only a small portion of the weights needs to be updated instead\n219 of rewriting all weights of the memristor array for a new pressure pattern, as shown in\n221 and practical, as the on-chip learning of memristor arrays has been demonstrated in the\n222 previous work45.”\n223 [Page 11, Lines 256-261]\n224\n225 “45. Zhang W. et al. Edge learning using a fully integrated neuro-inspired memristor\n226 chip. Science 381, 1205-1211 (2023).”\n227 [Page 25, Lines 587-588]\n228\n229 (6) The authors should clearly explain how the amplitude and frequency parameters\n230 were determined for each memristor array. The current discussion is unclear and lacks\n231 sufficient depth.\n233 and frequencies of the input signals are chosen based on the experimental equipment\n234 and memristive hardware, so that the parallel matrix multiplication in the frequency\n235 domain can implemented in a reliable way. In this way, the amplitudes of the input\n236 voltage signals are constrained below the half of the memristor’s switching threshold\n237 voltage to ensure the precision and accuracy. In experiments, an amplitude of 0.1 V was\n238 employed for the first memristor crossbar array. The amplitude applied to the second\n239 memristor crossbar array is determined by both the actual pressure pattern and the\n240 capacitance parameter of the amplifying circuit. We selected the capacitance value of\n241 the amplifying circuit comparable to that of the flexible capacitive pressure sensor.\n243 maintain below half of the memristor’s switching threshold voltage, avoiding the\n244 unintended resistance-state switching.\n246 selected to remain below the analog bandwidth limit of the overall system. Previous\n247 studies have demonstrated that memristors can exhibit bandwidths exceeding tens of\n248 gigahertz [Nature Communications 6, 7519 (2015)]. Thus, the practical bandwidth\n249 limitation arises from the amplifying circuit rather than the memristor devices in our\n250 system. The operational amplifier used in our setup (OPA891, Texas Instruments)\n251 provides a gain–bandwidth product of approximately 180 MHz, allowing us to flexibly\n252 select carrier frequencies well within this range for reliable frequency-division\n253 multiplexing operation.\n256 provided below, which is highlighted in yellow.\n257\n258 “…The amplitudes and frequencies of the input signals are chosen based on the\n259 experimental equipment and memristive hardware, so that the parallel matrix\n260 multiplication in the frequency domain can implemented in a reliable way. In this way,\n261 the amplitudes of the input voltage signals are constrained below the half of the\n262 memristor’s switching threshold voltage to ensure the precision and accuracy. In\n263 experiments, an amplitude of 0.1 V was employed for the first memristor crossbar array.\n265 the actual pressure pattern and the capacitance parameter of the amplifying circuit. We\n266 selected the capacitance value of the amplifying circuit comparable to that of the\n267 flexible capacitive pressure sensor. Thereby, the resulting input voltages amplitudes of\n268 the second memristor array also maintain below half of the memristor’s switching\n269 threshold voltage, avoiding the unintended resistance-state switching.\n271 selected to remain below the analog bandwidth limit of the overall system. Previous\n272 studies have demonstrated that memristors can exhibit bandwidths exceeding tens of\n273 gigahertz48. Thus, the practical bandwidth limitation arises from the amplifying circuit\n274 rather than the memristor devices in our system. The operational amplifier used in our\n275 setup (OPA891, Texas Instruments) provides a gain–bandwidth product of\n276 approximately 180 MHz, allowing us to flexibly select carrier frequencies well within\n277 this range for reliable frequency-division multiplexing operation.”\n278 [Page 19, Lines 437-456]\n279\n280 “48. Pi S. et al. Nanoscale memristive radiofrequency switches. Nat. Commun. 6, 7519\n281 (2015).”\n282 [Page 25, Lines 594-595]\n283\n284 (7) It appears that the system operations depicted in Figure 4 are based solely on\n285 simulations. If so, the authors should detail how the simulation parameters were set. If\n286 the results are experimental, the integration process for each system component should\n287 be clearly described. In either case, comprehensive measurement results and a\n288 component-by-component analysis are required.\n290 operations displayed in Fig. 4 are based on simulations. As discussed in the main\n291 manuscript, the textural feature extraction through two-dimensional discrete Fourier\n292 transform in Fig. 4 is to basically carry out three-matrix multiplications: T = ACA -\n294 the resulting matrix T is displayed through Python Matplotlib toolbox. The simulation\n295 parameters of the matrix A and B are set according to the matrix’s values shown in the\n296 main text. The simulation parameters of the matrix C are set by the pressure pattern\n297 with values of each element normalized into the scope of 0-1. Note that the operational\n298 principles in hardware implementation of the above three-matrix multiplications is the\n299 same as the two-matrix multiplication, which has already been demonstrated in\n300 experiments as shown in Fig. 3.\n302 simulation process into the Method section of the revised manuscript. For ease of\n303 reviewing, the revision has been provided below, which is highlighted in yellow.\n304\n305 “…The textural feature extraction through two-dimensional discrete Fourier transform\n306 in Fig. 4 is to basically carry out three-matrix multiplications: T = ACA - BCB. The\n307 above matrix multiplications are performed by Python NumPy toolbox and the resulting\n308 matrix T is displayed through Python Matplotlib toolbox. The simulation parameters of\n309 the matrix A and B are set according to the matrix’s values shown in the main text. The\n310 simulation parameters of the matrix C are set by the pressure pattern with values of\n311 each element normalized into the scope of 0-1. Note that the operational principles in\n312 hardware implementation of the above three-matrix multiplications is the same as the\n313 two-matrix multiplication, which has already been demonstrated in experiments as\n314 shown in Fig. 3.”\n315 [Page 20, Lines 473-482]\n316\n317 (8) The proposed parallel textural feature extraction of complex pressure patterns in\n319 processing. The authors should demonstrate more realistic and practical applications\n320 beyond mere extraction of textural features.\n322 proposed parallel textural feature extraction of complex pressure patterns in Fig. 4 is\n323 practically useful in real applications, which is pivotal for downstream object material\n324 recognition in agile gripping action of the robotic hand since the object material is\n325 directly correlated to its surface textural feature. The textural feature extraction\n326 transforms original pressure patterns into sparse spatial-frequency information as\n327 shown in Fig. 4b-4c, reducing the total data volume that needs to be transmitted and\n328 improving the anti-interference capability during data transmission. Meanwhile, the\n329 recognition accuracy based on compressed sparse spatial-frequency information is\n330 comparable to that of original pressure patterns. To further demonstrate the robustness\n331 of recognition accuracy based on spatial-frequency information, we have supplemented\n332 the recognition process following the textural feature extraction, with the results shown\n333 in Fig. R9. The original pressure patterns and the corresponding transformed spatial-\n334 frequency patterns after two-dimensional discrete Fourier transform are displayed in\n336 layer neural network, and the training and testing processes are achieved through\n338 on spatial-frequency information is comparable to that of original images, which\n339 demonstrates the robustness and effectiveness of patterns recognition based on spatial-\n340 frequency information.\n341\n343 and the corresponding spatial-frequency information (b) after two-dimensional Fourier\n344 transform. The dataset comprises five classes including periodic patterns stripes and\n345 grids, and nonperiodic patterns circles, rectangles, and triangles. Each class has 200\n346 different binarized images for training and 40 different binarized images for testing.\n348 of original images (c).\n15\n349\n352 the revision has been provided below, which is highlighted in yellow.\n353\n354 “…Furthermore, we have performed the recognition task based on the dataset\n355 comprising both periodic patterns classes and nonperiodic patterns classes. The original\n356 pressure patterns and the corresponding spatial-frequency patterns after two-\n357 dimensional Fourier transform are displayed in Fig. 4d and 4e, respectively. Neural\n358 network recognition details are provided in the Method section. According to the\n359 results shown in Fig. 4f, the recognition accuracy based on spatial-frequency\n360 information is comparable to that of original images, which demonstrates the robustness\n361 and effectiveness of patterns recognition based on spatial-frequency information.”\n362 [Pages 14-15, Lines 334-342]\n363\n364 “…d, Dataset of pressure patterns including five classes with both periodic classes and\n365 nonperiodic classes. e, The corresponding spatial-frequency patterns after two-\n366 dimensional discrete Fourier transform of the original pressure patterns in (d). f, The\n367 recognition accuracy for both original patterns and transformed spatial-frequency\n368 patterns across epochs.”\n369 [Page 16, Lines 353-358]\n370\n371 “…To better demonstrate the potential and usability in real applications, the\n372 downstream pattern recognition process following the textural feature extraction is\n373 performed. The dataset comprises five classes including periodic patterns stripes and\n374 grids, and nonperiodic patterns circles, rectangles, and triangles. Each class has 200\n375 different binarized images for training and 40 different binarized images for testing.\n377 information of original images after two-dimensional discrete Fourier transform,\n378 respectively. The recognition is performed by a simple two-layer neural network, and\n379 the training and testing processes are achieved through PyTorch.”\n380 [Pages 20-21, Lines 483-491]\n381\nThe work by Li et al. entitled “Skinomorphic in-sensor massively parallel computing”\n(Manuscript ID: NCOMMS-25-42827-T) reports a Merkel cell inspired parallel\ncomputing paradigm for intelligent tactile perception. The scheme employs continuous-\ntime analog reading signals across multiple frequencies for simultaneously sensing and\nprocessing based on frequency division multiplexing (FDM). The authors have\ndemonstrated one-shot sensing and processing of both perfect and broken pressure\npatterns ‘NJU’ by using 32×32 flexible capacitive pressure sensors array networked\nwith a memristive crossbar array. Furthermore, textural features from the complex\nloaded pressure patterns can also be effectively extracted in a parallel manner by\nconnecting the capacitive sensors array with two memristive crossbar array. The work\nprovides a universal and advanced tactile sensing scheme for real-time and high-\nthroughput tactile perception over previous technologies. Overall, the work is novel and\nthe experimental data can support the conclusions of the article. The work is also very\ninteresting and inspiring to me. Additionally, the paper is well-organized and well\nwritten. Therefore, I strongly recommend it for publication in the prestigious journal\nNature Communications, after addressing the following minor questions.\nResponse: We sincerely thank the reviewer for his/her very positive comments on the\nnovelty and impact of our manuscript. We also greatly appreciate the recommendation\nfor publication. Following the reviewer’s suggestions, we have provided additional\ndiscussions, clarifications, and simulations to address the reviewer’s concerns, as\ndetailed below.\n(1) Inspired by the mechanism and function of biological skin, the authors have\nproposed an innovative tactile in-sensor computing paradigm with high parallelism.\nThe word “skinomorphic” appropriately describes the unique feature of the proposed\nscheme. The discussion about structure and function of the biological skin especially\nMerkel cell that acts as one main type of tactile end organ cell, is relatively little in the\nmanuscript. The authors are suggested to supplement more elaborations regarding the\n18\ndetailed mechanism and functionality of the Merkel cell and the human skin.\nResponse: Thanks for the reviewer’s valuable suggestion. To enhance the clarity of the\nmanuscript and readability for readers, we have revised the relevant sentences and\nexpanded the discussions in the revised manuscript.\nTo reflect the reviewer’s suggestion, we have added more discussions and\nelaborations into the revised manuscript. For ease of reviewing, the revision has been\nprovided below, which is highlighted in yellow.\n“…The Merkel disc is a specialized tactile end organ formed by the synaptic connection\nbetween a Merkel cell and an Aβ-afferent ending41-44. As a key mechanoreceptor in the\nskin, the Merkel disc is responsible for sensing gentle touch and playing an essential\nrole in complex sensory tasks such as perceiving edges and textures. Meanwhile,\nfunctioning as a slowly adapting type I (SAI) receptor, the Merkel discs encode\nmechanical pressure of spatial features such as edges and textures into bioelectrical\nsignals.”\n[Page 4, Lines 79-85]\n(2) The hardware implementation of skinomorphic in-sensor parallel computing is\nbased on a capacitive sensors array networked with a memristor array. The descriptions\nof how to operate the one-transistor-one-memristor (1T1R) array is brief in the\nmanuscript. The authors are suggested to supplement more details on memristor array\noperations such as the conductance configuration in the Method section.\nResponse: Thanks for the reviewer’s helpful suggestion. To reliably realize the\nconductance configuration of memristor crossbar array, we gradually programmed the\nconductance values of the memristive devices by applying control signals by an MCU\nand programming voltage pulses through an Agilent B1530 pulse generator, where the\ncontrol signals are used for selecting the individual memristors by switching the\ntransistor and the voltage pulses are employed for tuning memristor conductance\ngradually. Accordingly, we programmed the memristor conductance into a defined\nrange using a closed-loop writing method implemented with a script in LabVIEW: if\nG < (G – ∆G), a positive pulse would be applied on the memristive device; else\nread target\nif G > (G + ∆G), a negative pulse would be applied.\nThe deviations of the\nread target\nprogrammed conductance values are controlled within about 5.0 μS for each\nconductance level. Note that the conductance variation can be further reduced by\nreducing appropriate initial parameters for ∆G used in the closed-loop writing method.\nTo reflect the reviewer’s suggestion, we have added more details into the Method\nsection of the revised manuscript. For ease of reviewing, the revision has been provided\nbelow, which is highlighted in yellow.\n“…To reliably realize the conductance configuration of memristor crossbar array, we\ngradually programmed the conductance values of the memristive devices by applying\ncontrol signals by an MCU and programming voltage pulses through an Agilent B1530\npulse generator, where the control signals are used for selecting the individual\nmemristors by switching the transistor and the voltage pulses are employed for tuning\nmemristor conductance gradually. Accordingly, we programmed the memristor\nconductance into a defined range using a closed-loop writing method implemented with\na script in LabVIEW: if G < (G – ∆G), a positive pulse would be applied on the\nread target\nmemristive device; else if G > (G + ∆G), a negative pulse would be applied.\nThe\nread target\ndeviations of the programmed conductance values are controlled within about 5.0 μS\nfor each conductance level. Note that the conductance variation can be further reduced\nby reducing appropriate initial parameters for ∆G used in the closed-loop writing\nmethod.”\n[Page 18, Lines 416-428]\n(3) The memristor array is responsible for representing the weight matrix Vkm that\nmultiplies with the capacitance matrix in the hardware implementation. The\nexperimental details of how continuous-time voltage signals with multiple frequencies\nare fed into the memristor array and the readout method need to be further elaborated\n20\nin the Method section, so that the importance of using the memristor can be highlighted.\nResponse: Thanks for the reviewer’s valuable suggestion. In the hardware\nimplementation, continuous-time voltage signals with identical amplitude and different\nfrequencies are generated by the voltage-controlled oscillators. These carrier wave\nsignals are simultaneously fed into the rows of the memristor crossbar array in parallel.\nThe output signals of the memristor array are then fed into the rows of the capacitive\nsensor array for multiplication through the amplifying circuit. To obtain the frequency\ncomponents from the output signals of the sensor array, we employ a digital\noscilloscope equipped with a Fourier transform function, which can clearly distinguish\ndifferent frequencies and read them out.\nThe amplitudes and frequencies of the input signals are chosen based on the\nexperimental equipment and memristive hardware, so that the parallel matrix\nmultiplication in the frequency domain can implemented in a reliable way. In this way,\nthe amplitudes of the input voltage signals are constrained below the half of the\nmemristor’s switching threshold voltage to ensure the precision and accuracy. In\nexperiments, an amplitude of 0.1 V was employed for the first memristor crossbar array.\nThe amplitude applied to the second memristor crossbar array is determined by both\nthe actual pressure pattern and the capacitance parameter of the amplifying circuit. We\nselected the capacitance value of the amplifying circuit comparable to that of the\nflexible capacitive pressure sensor. Thereby, the resulting input voltages amplitudes of\nthe second memristor array also maintain below half of the memristor’s switching\nthreshold voltage, avoiding the unintended resistance-state switching.\nAs for the choice of frequency parameter, the highest frequency component is\nselected to remain below the analog bandwidth limit of the overall system. Previous\nstudies have demonstrated that memristors can exhibit bandwidths exceeding tens of\ngigahertz [Nature Communications 6, 7519 (2015)]. Thus, the practical bandwidth\nlimitation arises from the amplifying circuit rather than the memristor devices in our\nsystem. The operational amplifier used in our setup (OPA891, Texas Instruments)\nprovides a gain–bandwidth product of approximately 180 MHz, allowing us to flexibly\n21\nselect carrier frequencies well within this range for reliable frequency-division\nmultiplexing operation.\nTo reflect the reviewer’s suggestion, we have added more elaborations into the\nMethod section of the revised manuscript. For ease of reviewing, the revision has been\nprovided below, which is highlighted in yellow.\n“…In the hardware implementation, continuous-time voltage signals with identical\namplitude and different frequencies are generated by the voltage-controlled oscillators.\nThese carrier wave signals are simultaneously fed into the rows of the memristor\ncrossbar array in parallel. The output signals of the memristor array are then fed into\nthe rows of the capacitive sensor array for multiplication through the amplifying circuit.\nTo obtain the frequency components from the output signals of the sensor array, we\nemploy a digital oscilloscope equipped with a Fourier transform function, which can\nclearly distinguish different frequencies and read them out.\nThe amplitudes and frequencies of the input signals are chosen based on the\nexperimental equipment and memristive hardware, so that the parallel matrix\nmultiplication in the frequency domain can implemented in a reliable way. In this way,\nthe amplitudes of the input voltage signals are constrained below the half of the\nmemristor’s switching threshold voltage to ensure the precision and accuracy. In\nexperiments, an amplitude of 0.1 V was employed for the first memristor crossbar array.\nThe amplitude applied to the second memristor crossbar array is determined by both\nthe actual pressure pattern and the capacitance parameter of the amplifying circuit. We\nselected the capacitance value of the amplifying circuit comparable to that of the\nflexible capacitive pressure sensor. Thereby, the resulting input voltages amplitudes of\nthe second memristor array also maintain below half of the memristor’s switching\nthreshold voltage, avoiding the unintended resistance-state switching.\nAs for the choice of frequency parameter, the highest frequency component is\nselected to remain below the analog bandwidth limit of the overall system. Previous\nstudies have demonstrated that memristors can exhibit bandwidths exceeding tens of\n22\ngigahertz48. Thus, the practical bandwidth limitation arises from the amplifying circuit\nrather than the memristor devices in our system. The operational amplifier used in our\nsetup (OPA891, Texas Instruments) provides a gain–bandwidth product of\napproximately 180 MHz, allowing us to flexibly select carrier frequencies well within\nthis range for reliable frequency-division multiplexing operation.”\n[Pages 18-19, Lines 429-456]\n“48. Pi S. et al. Nanoscale memristive radiofrequency switches. Nat. Commun. 6, 7519\n(2015).”\n[Page 25, Lines 494-495]\n(4) The authors have experimentally demonstrated the textural feature extraction for\nboth periodic pressure patterns and non-periodic pressure patterns in Figure 4, which\nhas verified the capability and feasibility of the proposed scheme. The authors are also\nencouraged to further perform the recognition/classification of the complex pressure\npatterns based on the extracted textural features and make a comparison with that\ndirectly based on the original pressure patterns.\nResponse: Thanks for the reviewer’s constructive suggestion. By following the\nreviewer’s suggestion, we have performed the downstream recognition task simulation\nbased on the extracted textural features and the original pressure patterns, with the\ncorresponding results shown in Fig. R10. The original pressure patterns and the\ncorresponding transformed spatial-frequency patterns after two-dimensional discrete\nFourier transform are displayed in Fig. R10a and Fig. R10b respectively. The\nrecognition is performed by a simple two-layer neural network, and the training and\ntesting processes are achieved through PyTorch. According to the results shown in Fig.\nR10c, the recognition accuracy based on spatial-frequency information is comparable\nto that of original images, which demonstrates the robustness and effectiveness of\npatterns recognition based on spatial-frequency information.\nthe corresponding spatial-frequency information (b) after two-dimensional Fourier\ntransform. The dataset comprises five classes including periodic patterns stripes and\ngrids, and nonperiodic patterns circles, rectangles, and triangles. Each class has 200\ndifferent binarized images for training and 40 different binarized images for testing.\nThe recognition accuracy based on spatial-frequency information is comparable to that\nof original images (c).\nTo reflect the reviewer’s suggestion, we have added the Fig. R10 into the revised\nFig. 4 as Fig. 4d-f and updated the corresponding figure captions. For ease of reviewing,\nthe revision has been provided below, which is highlighted in yellow.\n“…Furthermore, we have performed the recognition task simulation based on the\ndataset comprising both periodic patterns classes and nonperiodic patterns classes. The\noriginal pressure patterns and the corresponding spatial-frequency patterns after two-\ndimensional Fourier transform are displayed in Fig. 4d and 4e, respectively. Neural\nnetwork recognition details are provided in the Method section. According to the\nresults shown in Fig. 4f, the recognition accuracy based on spatial-frequency\ninformation is comparable to that of original images, which demonstrates the robustness\nand effectiveness of patterns recognition based on spatial-frequency information.”\n[Pages 14-15, Lines 334-342]\n“…d, Dataset of pressure patterns including five classes with both periodic classes and\n24\nnonperiodic classes. e, The corresponding spatial-frequency patterns after two-\ndimensional discrete Fourier transform of the original pressure patterns in (d). f, The\nrecognition accuracy for both original patterns and transformed spatial-frequency\npatterns across epochs.”\n[Page 16, Lines 353-358]\n“…To better demonstrate the potential and usability in real applications, the\ndownstream pattern recognition process following the textural feature extraction is\nperformed. The dataset comprises five classes including periodic patterns stripes and\ngrids, and nonperiodic patterns circles, rectangles, and triangles. Each class has 200\ndifferent binarized images for training and 40 different binarized images for testing.\nThe recognition is based on the original images and the transformed spatial-frequency\ninformation of original images after two-dimensional discrete Fourier transform,\nrespectively. The recognition is performed by a simple two-layer neural network, and\nthe training and testing processes are achieved through PyTorch.”\n[Pages 20-21, Lines 383-391]\n(5) Some minor corrections: The units and color bars of Figure 4b and 4c are missing\nand the authors need to add them in.\nResponse: We greatly appreciate the reviewer’s careful reading and the helpful\nfeedback. The results of Fig. 4b and 4c are based on simulation. The color scale\nrepresents relative intensities of original patterns and the two-dimensional spatial-\nfrequency spectrum rather than the absolute measured intensities. Therefore, the units\nare arbitrary units (a.u.) and the color bars are supplemented into Fig. 4b and 4c.\nTo reflect the reviewer’s suggestion, we have supplemented the units and color bars\ninto Fig. 4b and 4c. For ease of reviewing, the revision has been provided below, which\nis highlighted in yellow.\n“…The units of (b)-(e) are arbitrary units (a.u.) with normalization into 0-1.”\n25\n[Page 16, Lines 357-358]\nIn this work, Li et al. present a novel in-sensor computing paradigm for massively\nparallel tactile perception, which is inspired by the Merkle cells of the human skin. The\nparadigm utilizes the continuous-time data representation and frequency division\nmultiplexing to realize parallel reading and processing of pressure pattern, overcoming\nthe challenge of traditional tactile sensing schemes. The authors fabricated a 32×32\nflexible capacitive pressure sensors array and networked it with a memristive crossbar\narray, and experimentally demonstrated one-shot sensing and restoration of the broken\npressure patterns ‘NJU’. Furthermore, they demonstrated textural features of some\ntypical complex pressure patterns by using two memristor array and the capacitive\npressure sensors array. The study offers an in-sensor parallel computing scheme for\nhighly efficient large-scale tactile information perception in real-time, revolutionizing\nthe traditional tactile sensing and perception. This work is timely and significant to the\nfield of intelligent tactile sensing. Therefore, I would recommend it for publication after\nminor revisions.\nResponse: We greatly thank the reviewer for appreciating the novelty and impact of\nour work, and recommending it for publication. In response to the reviewer’s questions,\nwe have provided additional clarifications and simulations as detailed below.\n(1) The parallel readout and processing of broken pressure pattern as shown in Fig. 3f\nis really interesting. I note that the units of the weights for restoration in Figure 3f are\nvoltage, but the units of the weights for memristors array are typically conductance.\nPlease provide relevant explanations on reason why the voltage unit is used for the\nweights.\nResponse: We sincerely appreciate the reviewer’s careful reading and helpful feedback.\nIn this work, we present an in-sensor skinomorphic computing paradigm in which\nmemristor arrays function as the peripheral circuitry for the capacitive tactile sensor\narray. The primary role of the memristor array is loading and representing the weight\nmatrix on the frequency spectrum of the voltage carrier waves for multiplication with\n27\nthe subsequent pressure pattern matrix of the sensor array. Consistent with the voltage-\nbased input of the sensor array, the output of the memristor array is also expressed in\nvoltage units, which ensures seamless integration with subsequent signal processing\nand maintains compatibility within the overall parallel computing framework.\n(2) The textural feature extraction based on in-sensor parallel Fourier transform in\nFigure 4 is interesting to me. Particularly, the textural feature extraction enables robust\nrecognition of the pressure pattern under compressed data volume. Such advantage can\nbe further highlighted by providing the comparison of recognition accuracy between\nthe processed pattern based on the authors’ approach and original pressure patterns.\nResponse: We greatly thank the reviewer for the constructive suggestion. By following\nthe reviewer’s guidance, we have performed the downstream recognition task\nsimulation based on the extracted textural features and the original pressure patterns,\nwith the recognition accuracy results shown in Fig. R11. The original pressure patterns\nand the corresponding transformed spatial-frequency patterns after two-dimensional\ndiscrete Fourier transform are displayed in Fig. R11a and Fig. R11b, respectively. The\nrecognition is performed by a simple two-layer neural network, and the training and\ntesting processes are achieved through PyTorch. According to the results shown in Fig.\nR11c, the recognition accuracy based on spatial-frequency information is comparable\nto that of original images, which demonstrates the robustness and effectiveness of\npatterns recognition based on spatial-frequency information.\nFig. R11. Simulations of pressure patterns recognition for both original patterns (a) and\nthe corresponding spatial-frequency information (b) after two-dimensional Fourier\n28\ntransform. The dataset comprises five classes including periodic patterns stripes and\ngrids, and nonperiodic patterns circles, rectangles, and triangles. Each class has 200\ndifferent binarized images for training and 40 different binarized images for testing.\nThe recognition accuracy based on spatial-frequency information is comparable to that\nof original images (c).\nTo reflect the reviewer’s suggestion, we have added the Fig. R11 into the revised\nFig. 4 as Fig. 4d-f and updated the corresponding figure captions. For ease of reviewing,\nthe revision has been provided below, which is highlighted in yellow.\n“…Furthermore, we have performed the recognition task based on the dataset\ncomprising both periodic patterns classes and nonperiodic patterns classes. The original\npressure patterns and the corresponding spatial-frequency patterns after two-\ndimensional Fourier transform are displayed in Fig. 4d and 4e, respectively. Neural\nnetwork recognition details are provided in the Method section. According to the\nresults shown in Fig. 4f, the recognition accuracy based on spatial-frequency\ninformation is comparable to that of original images, which demonstrates the robustness\nand effectiveness of patterns recognition based on spatial-frequency information.”\n[Pages 14-15, Lines 334-342]\n“…d, Dataset of pressure patterns including five classes with both periodic classes and\nnonperiodic classes. e, The corresponding spatial-frequency patterns after two-\ndimensional discrete Fourier transform of the original pressure patterns in (d). f, The\nrecognition accuracy for both original patterns and transformed spatial-frequency\npatterns across epochs.”\n[Page 16, Lines 353-358]\n“…To better demonstrate the potential and usability in real applications, the\ndownstream pattern recognition process following the textural feature extraction is\n29\nperformed. The dataset comprises five classes including periodic patterns stripes and\ngrids, and nonperiodic patterns circles, rectangles, and triangles. Each class has 200\ndifferent binarized images for training and 40 different binarized images for testing.\nThe recognition is based on the original images and the transformed spatial-frequency\ninformation of original images after two-dimensional discrete Fourier transform,\nrespectively. The recognition is performed by a simple two-layer neural network, and\nthe training and testing processes are achieved through PyTorch.”\n[Pages 20-21, Lines 483-491]\n(3) The statistical data of Figure 2d illustrates the good uniformity of the fabricated\ncapacitive sensors array. To quantify the uniformity, the authors are suggested to\ncalculate the mean value and the standard deviation.\nResponse: We sincerely thank the reviewer for the useful suggestion. The calculated\nmean value (μ) and the standard deviation (σ) of the responsivity (ΔC/C ) for 1024\n0\ndevices in the array are 19.31% and 2.38%, respectively. By following the reviewer’s\nsuggestion, we have added the calculated mean value and standard deviation in Fig. 2d\nto better quantify the uniformity.\nTo reflect the reviewer’s suggestion, we have added the calculation results into Fig.\n2d and the revised manuscript. For ease of reviewing, the revision has been provided\nbelow, which is highlighted in yellow.\n“…The calculated mean value (μ) and the standard deviation (σ) of the responsivity\n(ΔC/C ) for 1024 devices in the array are 19.31% and 2.38%, respectively.”\n0\n[Page 10, Lines 215-216]\n30"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I have carefully reviewed the authors’ responses. Unfortunately, many of the previously raised concerns remain unresolved. First, the authors indicate that they used conventional capacitive pressure sensors and memristors with no exceptional performance, focusing instead on demonstrating in-sensor skinomorphic computing. However, the core concept—integrating capacitive sensors and memristors using AC signals—appears to have been reported previously [J.-Y. Cho et al., npj Flex. Electron. 9, 34 (2025)]. Although an array-scale tactile sensing system is claimed, the demonstration relies heavily on simulations, making it difficult to assess the manuscript’s novelty and experimental rigor relative to the standards of Nature Communications.\nSecond, the comparative evaluation is limited to a conventional tactile sensing pipeline with sequential processing. To convincingly establish novelty, the proposed architecture should be benchmarked against recently reported state-of-the-art systems that realize similar functionality at the system level, with quantitative, like-for-like comparisons (in terms of latency, energy efficiency, and wiring complexity).\nThird, practical deployment of the proposed in-sensor skinomorphic computing requires optimization of AC signal conditions and combinations, as well as evidence of array-level uniformity across cells. The manuscript does not provide sufficient experimental data on these points. As a result, the current results are insufficient to substantiate the claimed advances.\nFor these reasons, I cannot recommend acceptance.", "reviewer_3": "The authors have well addressed all my concerns. I recommend the acceptance of this publication."}, "author_response": "Response to Referees’ Comments\nReviewer #1 (Remarks to the Author):\nI have carefully reviewed the authors’ responses. Unfortunately, many of the previously\nraised concerns remain unresolved. First, the authors indicate that they used\nconventional capacitive pressure sensors and memristors with no exceptional\nperformance, focusing instead on demonstrating in-sensor skinomorphic computing.\nHowever, the core concept—integrating capacitive sensors and memristors using AC\nsignals—appears to have been reported previously [J.-Y. Cho et al., npj Flex.\nElectron. 9, 34 (2025)]. Although an array-scale tactile sensing system is claimed, the\ndemonstration relies heavily on simulations, making it difficult to assess the\nmanuscript’s novelty and experimental rigor relative to the standards of Nature\nCommunications.\nResponse: We sincerely thank the reviewer for his/her careful review and interest in\nour manuscript. The core concept of our work is to fundamentally transform the in-\nsensor computing scheme from the time domain to the frequency domain by using\ncontinuous-time data representation, which enables massively high parallel readout and\nprocessing for all pixels of the array simultaneously. This conceptual innovation leads\nto ultrahigh throughput and ultralow latency with significantly reduced complexity of\nwiring especially when the array size is scaled up, substantially breaking the upper limit\nof conventional time-serial in-sensor computing. We chose the integrated capacitive\nsensor array and memristor array with AC signals just for experimentally demonstrating\nour proof-of-concept in-sensor parallel tactile computing.\nWe have carefully studied the previous works of in-sensor/near-sensor computing,\nand we can conclude that the concept of our work has never been reported previously.\nThe work [J.-Y. Cho et al., npj Flex. Electron. 9, 34 (2025)] mentioned by the reviewer\nreports a starfish-inspired tactile near-sensor computing system that integrates\ncapacitive pressure sensors with flexible memristors to transduce touch into transient\n1\nelectrical signals for energy-efficient processing, demonstrating applications in\nfingerprint recognition and artificial nociception. The underlying mechanisms and\nmethods as well as applications of this work are totally different from our work. To\nfurther substantiate the conceptual advance and convincingly establish the novelty of\nour work, we followed the reviewer’s suggestion to perform additional experiments and\nmake a comprehensive comparison with recently reported state-of-the-art systems\nincluding the one mentioned by the reviewer, as detailed below.\nSecond, the comparative evaluation is limited to a conventional tactile sensing pipeline\nwith sequential processing. To convincingly establish novelty, the proposed architecture\nshould be benchmarked against recently reported state-of-the-art systems that realize\nsimilar functionality at the system level, with quantitative, like-for-like comparisons (in\nterms of latency, energy efficiency, and wiring complexity).\nResponse: Thanks for the reviewer’s constructive suggestion. Following the reviewer’s\nsuggestion, we have made a systematic quantitative comparison and benchmarked our\nproposed scheme against recently reported tactile sensory systems, as displayed in\nTable R1. Our in-sensor parallel computing architecture achieves superior performance\nover state-of-the-art systems in terms of equivalent wiring complexity, throughput,\nequivalent latency, and energy efficiency.\nTo have a fair comparison, we have normalized all prior works with different\nmechanisms and array sizes to a same baseline. The equivalent wiring complexity is\ndefined as the number of I/O ports required for one-shot readout of all N×N pixels\nwithin the array in parallel. The throughput is defined as the number of operations per\nsecond (OPS), quantifying how many computations the array performs per unit time.\nWe assume a common device operation frequency of 1 MHz (106 OPS) for all works.\nAccordingly, the throughput equals to the number of devices operating concurrently\nwithin the array multiplied by this common frequency. The equivalent latency is defined\nas the time required to process a single 64×64 tactile pattern at 1 MHz operation\nfrequency, which can be calculated by the reciprocal of the throughput multiplied by\n2\n4096. The energy efficiency is given as the throughput divided by the array’s power\nconsumption (OPS/W). The power consumption values of previous works are directly\nsourced from the literature, and the power consumption of our capacitive pressure\nsensor array can be estimated by 1024×CV2f, in which C = 6 pF, V = 60 mV, f = 5 kHz.\nTable R1. Comparisons of our work with recently reported tactile sensory systems\nin terms of equivalent wiring complexity, throughput, equivalent latency, and\nenergy efficiency.\nEquivalent\nEquivalent\nParallelism wiring Throughput\nlatency Energy\nReference (For all pixels in complexity (OPS at 1\n(ms at 1 efficiency\nthe array) (For N×N MHz) MHz)\npixels array)\n[1] 6.4×107 6.4×10-2 /\n[2] / / / \n~0.7\n[3] Sequential 4×106 1.024\nGOPS/W\nprocessing in the\ntime domain N2\n(Row by row/\n~18\n[4] Patch by patch) 9×106 0.455\nGOPS/W\n~460\n[5] 9×106 0.455\nGOPS/W\n~7143\n[6] 9×106 0.455\nTOPS/W\nParallel\nprocessing in the\n~9259\nOur work frequency 2N 1.024×109 4×10-6\nTOPS/W\ndomain\n(One-shot)\n[1] Zhao Z. et al. Large-scale integrated flexible tactile sensor array for sensitive smart robotic touch.\nACS Nano 16, 16784 (2022).\n[2] Liu Z. et al. A three-dimensionally architected electronic skin mimicking human\nmechanosensation. Science 384, 987 (2024).\n[3] Gao Y. et al. A neuromorphic robotic electronic skin with active pain and injury perception. P.\n[4] Chen Y. et al. Capacitive in-sensor tactile computing. Nat. Commun. 16, 5691 (2025).\n[5] Wang M. et al. Tactile near-sensor analogue computing for ultrafast responsive artificial skin.\nAdv. Mater. 34, 2201962 (2022).\n[6] Cho J.-Y. et al. Tactile near-sensor computing systems incorporating hourglass-shaped\nmicrostructured capacitive sensors for bio-realistic energy efficiency. npj Flex. Electron. 9,\n34 (2025).\nTo reflect the reviewer’s suggestion, we have added Table R1 and the\ncorresponding discussions into the Supplementary Information as Supplementary\nNote Ⅰ. For ease of reviewing, the revision has been provided below, which is\nhighlighted in yellow.\n“…Comparisons of our work with recently reported state-of-the-art tactile sensory\nsystems are displayed in Supplementary Note Ⅰ.”\n[Page 11, Lines 271-272] in Main Manuscript.\nThird, practical deployment of the proposed in-sensor skinomorphic computing\nrequires optimization of AC signal conditions and combinations, as well as evidence of\narray-level uniformity across cells. The manuscript does not provide sufficient\nexperimental data on these points. As a result, the current results are insufficient to\nsubstantiate the claimed advances.\nFor these reasons, I cannot recommend acceptance.\nResponse: Thanks for the reviewer’s suggestion. To further demonstrate the practical\ndeployment of the proposed scheme, following the reviewer’s suggestion, we have\nperformed additional experiments on various AC signal conditions and the array-level\nuniformity. The capacitive pressure sensor is measured by applying AC voltages with\ndifferent frequencies under periodic pressure loading (Fig. R1). The sensor can\nsuccessfully sense the loaded pressure and convert the signal into identical capacitance\nchanges over a broad frequency range from 0-5 MHz (Note that the maximum\nfrequency of the voltage output of B1500 is 5 MHz). We have also measured the\nfrequency characteristics of the memristor by using a vector network analyzer (Fig. R2).\nIn conjunction with the frequency characteristics of the capacitive pressure sensor,\nour integrated system can be operated over a wide frequency range from 0-5 MHz, which\nsufficiently fulfills the requirement of practical deployment in hardware.\nWe have also evaluated the uniformity of a 32×32 capacitive pressure sensors array\nand the corresponding results are shown in Fig. 2d of the main manuscript. To evaluate\nthe uniformity of the memristors array, we mapped a specific conductance pattern into\nthe 32×32 array and measured the actual conductance values of the array, followed by\ncalculating the difference between the actual conductance and the value in the original\npattern of each memristor (Fig. R3). The statistical conductance differences (ΔG) of\nmemristors displayed in Fig. R3 indicate the excellent uniformity of the memristors\narray. These additional experimental data on AC signal conditions and uniformity\nfurther substantiate the practical deployment of our scheme.\nFigure R1. Capacitance change of the pressure sensor with applied AC voltages of\ndifferent frequencies. The pressure of 30 kPa is loaded onto the sensor periodically.\na vector network analyzer at different conductance levels (solid lines with varying\ncolors).\nFigure R3. Uniformity characterization of the 32×32 memristors array by writing a\nspecific conductance pattern into the array. Left: Histogram of original conductance\nvalues of the pattern. Right: Histogram of the conductance differences (ΔG) of the\nmemristors in the 32×32 array, in which the ΔG reflects the differences between the\nexpected conductance value and the programmed conductance value. The narrow\ndistribution in the histogram indicates that all devices can be well programmed. The red\nline is the Gaussian fit curve of the distribution.\nTo reflect the reviewer’s suggestion, we have added Figure R1 into the\nrevision has been provided below, which is highlighted in yellow.\n“…We also measured the sensor by applying alternating-current (AC) voltages with\ndifferent frequencies under periodic pressure loading (Supplementary Fig. S2). The\nsensor can successfully sense the loaded pressure and convert the signal into identical\ncapacitance changes over a broad frequency range from 0-5 MHz.”\n[Page 8, Lines 193-197] in Main Manuscript.\nWe have added Figure R2-R3 into the Supplementary Information as Supplementary\nFig. S11-S12. For ease of reviewing, the revision has been provided below, which is\nhighlighted in yellow.\n“…More detailed performance data for the memristor and their array including\nuniformity and frequency characteristics are shown in Supplementary Fig. S7-S12. In\nconjunction with the frequency characteristics of the capacitive pressure sensor\n(Supplementary Fig. S2), our integrated system can be operated over a wide frequency\nrange from 0-5 MHz, which sufficiently fulfills the requirement of practical deployment\nin hardware.”\n[Pages 10-11, Lines 240-245] in Main Manuscript.\nThe authors have well addressed all my concerns. I recommend the acceptance of this\npublication.\nResponse: We thank the reviewer for recommending it for publication.\n8"}]

Decision letter