Nano Energy 130 (2024) 110133

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Nano Energy
journal homepage: www.elsevier.com/locate/nanoen

Optically enhanced organic phototransistors for adaptive image processing
under complex light conditions
He Shao a, b , Yu Ji a , Ruiheng Wang a , Jing Liu a , Yueqing Li a , Benxin Liu a , Wen Li a ,Yannan Xie a , Linghai Xie a, * , Johnny C. Ho b, * , Wei Huang a, c , Haifeng Ling a, *aState Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications(NJUPT), Nanjing 210023, China
Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
cFrontiers Science Center for Flexible Electronics (FSCFE), MIIT Key Laboratory of Flexible Electronics (KLoFE), Northwestern Polytechnical University (NPU), Xi’an
710072, Chinab

A R T I C L E I N F O

A B S T R A C T

Keywords:
Image processing
Visuomorphic phototransistor
Organic heterostructures
Bidirectional conductanceLight harvesting

Effectively handling massive information in complex lighting conditions is crucial for security-focused machine
vision, as it relies on precise image feature detection and encryption/decryption to ensure integrity and confidentiality. Traditional hardware, primarily based on complementary metal-oxide-semiconductor (CMOS) technology, struggles with the complexity of processing tasks around the clock. To address this challenge, a ternaryorganic heterostructure visuomorphic phototransistor was tailored for adaptive critical image processing. The
light coupling induced by the porous heterostructural layer enhances the light absorption efficiency by 2.5-fold.
Our phototransistor exhibits bidirectional photoresponse across the visible and near-infrared (NIR) spectral regions, enabling adaptive edge detection in varying lighting conditions with precision above 85 %. The phototransistor array facilitates image encryption/decryption with a superior accuracy of 12 %/90 % compared toexisting counterparts. This ternary organic hetero-integration combines multiple response modes, enablingcompact and efficient critical feature processing.

1. Introduction
In recent years, artificial intelligence systems have assumed a pivotal
role in diverse facets of daily life, such as intelligent homes, autopilot
vehicles, and medical diagnosis, by facilitating image recognition andanalysis [1–3]. However, the partitioning of memory and processing
units in traditional von Neumann architecture has limited the speed andquality of data processing [4–6]. The efficient processing mode of thehuman visual system for image information can serve as a valuablereference [7]. Visual stimuli are adeptly captured by photoreceptors in
the retina and rapidly transmitted to the visual cortex of the brain forexpeditious and intricate processing (Fig. 1a) [8,9]. Inspired by humanvision, visuomorphic devices are expected to represent an efficientparadigm to meet the requirements of processing complex image information [10,11]. Researchers have developed image visual chips
based on silicon complementary metal-oxide-semiconductor (CMOS)
technology [12–14]. These innovations continue to grapple with challenges from circuit complexity, high energy consumption, limited

compatibility and integration [15–17]. Therefore, there is an urgent
necessity for novel hardware design and software processing approaches
to simplify information volume and accelerate processing tasks [18,19].
Critical feature processing can simplify extensive tasks and improve
processing efficiency. Focusing on extracting and identifying key features and structures contributes to optimizing and accelerating largescale image processing [20–22]. However, considering data security
issues, concealing critical features to enhance data confidentiality is alsoessential when handling critical data [23,24]. Therefore, in the face ofmassive and complex image information, focusing on critical information processing is imperative for expanding the functionality of visuomorphic devices [25]. In addition, it is worth noting that current
visuomorphic devices mainly rely on visible light response and capture
objects in bright environments for recognition. Broadening the photoresponse range and implementing multiple response modes enable themto adapt to more complex detection environments. Nevertheless, it isstill a challenge to realize bidirectional modulation of the entire visible
spectrum and NIR region for one hardware device, as it typically* Corresponding authors.
E-mail addresses: iamlhxie@njupt.edu.cn (L. Xie), johnnyho@cityu.edu.hk (J.C. Ho), iamhfling@njupt.edu.cn (H. Ling).
https://doi.org/10.1016/j.nanoen.2024.110133
Received 30 June 2024; Received in revised form 4 August 2024; Accepted 15 August 2024
Available online 20 August 2024
2211-2855/© 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

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manifests either a positive or negative response to a singular wavelength[26,27].
In this work, a ternary organic heterostructure phototransistor wasconstructed, comprising a donor-acceptor bulk-heterojunction (BHJ)
structure, along with a p-type semiconductor layer, as shown in Fig. 1b.
Notably, a three-dimensional tunable porous photosensitive layer isconstructed by a phase separation process (Fig. 1c). The Finitedifference time domain (FDTD) simulation results substantiate thecapability of the porous structure to enhance light absorption power by
2.5-fold (Fig. 1d). Additionally, this device concurrently manifests nonvolatile positive photoconductance (PPC) and volatile negative photoconductance (NPC) with threshold-switching characteristics for both
red, green, blue (RGB) and even near-infrared (NIR) spectra. This
remarkable wide-spectrum bidirectional photoresponse facilitates
adaptive edge detection with precision above 85 % in varying lighting
conditions. Subsequently, the phototransistor arrays are innovatively
used to encrypt and decrypt image data. The encryption and decryptionof image data with remarkable accuracy of 12 % and 90 % signify data
security assurance.

of 1,10-decanediol (DDO) and 1-chloronaphthalene (CN) were introduced to control the morphology of PM6 and Y7-BO, respectively. The
chemical structures of PM6, Y7-BO, DDO, and CN are exhibited inFig. S1. The additive contents of DDO and CN are confirmed to be 0.1 vol%, 0.25 vol%, 0.5 vol%, and 1 vol%, respectively, which are the volume
ratio of PM6 and Y7-BO solution, respectively. According to previous
reports, the addition of DDO improved the crystallinity of the polymer
donor PM6, while the CN enhanced the ordered crystallite structure of
acceptor Y7-BO molecule [28,29]. The crystallization properties ofpristine PM6 and Y7-BO without any additives on PS can be revealed byatomic force microscopy (AFM) results. As shown in Fig. S2, a dense andflat film was obtained upon spin-coating pristine PM6 onto PS andsubsequent annealing. With increasing content of DDO, there is adiscernible augmentation in grain size within the PM6 film, consequently leading to a concomitant elevation in crystallinity. When thepristine Y7-BO was spin-coated onto PS, as shown in Fig. S3, the introduction of a certain amount of CN caused Y7-BO to aggregate moreorderly and exhibit distinct borders. Fig. S4 shows the effect of DDO on
the PM6/Y7-BO BHJ film. The noticeable crystallization and porous
structures were still observed in the annealed film. However, these
porous structures were disordered and had varying depths and diameters, which could affect the subsequent growth of pentacene andthus reduce device performance. In contrast, when CN was further
introduced, the film exhibited more uniform pores with increased diameters. This indicates that the uniformly tunable porous structure of
the film is due to the synergistic effect of the two additives in the BHJ.
The morphological differences between BHJ with DDO and DDO/CN

2. Results and discussion
The phototransistor based on BHJ/pentacene was constructed withhighly doped silicon (Si) as the substrate pre-grown with a 300 nm-thick
SiO2 layer. Subsequently, a 30 nm-thick polystyrene (PS) was spincoated onto the substrate. The BHJ consists of PM6:Y7-BO with a volume ratio of 1:1, which was then formed on PS. In addition, the additives

Fig. 1. (a) The schematic diagram of the visual system’s processing and recognition of image information, as well as the process of image data security protection byvisuomorphic devices. The excitatory and inhibitory represent the bipolar plasticity in the biological vision system. (b) The structure of the phototransistor with the
BHJ/pentacene. The BHJ layer is composed of PM6, Y7-BO, CN and DDO. (c) The schematic diagram and the corresponding AFM image of the porous film. (d) The
simulated light absorption intensity enhancement distribution of BHJ/pentacene film with and without pores along the direction above the surface of BHJ/pentacene film.
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suggest that pore formation is induced by the excessive crystallization of
PM6 and the subsequent ordered crystallization of Y7-BO [30–32].
The diameters and depths of pores within BHJ films, modulated byvarying additive contents, are delineated in Fig. 2a-c, Figs. S5 and S6.
The pertinent statistical distributions are depicted in Fig. S7. In theabsence of additives, the resulting BHJ film displays a planarmorphology. Conversely, upon introducing 0.1 vol% additives,discernible pores within the BHJ film become apparent. Notably, both
the diameters and depths of these pores show augmentation as the additive contents escalated from 0.1 vol% to 1 vol%. Specifically, therespective average pore diameters were confirmed to be 150, 320, 420,
and 520 nm for additive contents of 0.1 vol%, 0.25 vol%, 0.5 vol%, and
1 vol%. This trend indicates a direct correlation between the additive
concentration and pore size, with higher additive contents leading tolarger pore diameters. Correspondingly, these pores’ mean depths were
determined to be 15, 22, 45, and 70 nm, reflecting a similar trend where
increased additive concentration results in deeper pores. The controlled
modulation of pore dimensions through additive concentration highlights the tunability of the BHJ film’s morphological properties, which
could be pivotal for optimizing the film’s performance in various applications. To ascertain that the observed pore formation stemmed fromthe enhanced aggregation of PM6 and ordered crystallization of Y7-BO
facilitated by the additives, dynamic X-ray photoelectron spectroscopy(DXPS) was employed for a comprehensive analysis of BHJ films acrossvarying additive contents. Fig. 2d-f elucidate the N 1 s depth profilingimages characteristic of the BHJ film. Notably, the N 1 s peak can beobserved when the combined energy approaches 399.1 eV, where thecolor intensity is related to the magnitude of the peak signal [33]. Nitrogen serves as the discernible constituent of Y7-BO, thereby providing
valuable insights into the spatial distribution and enrichment levels of
Y7-BO across varied film depths. The additive-free BHJ film exhibits
consistent N 1 s peak intensities across disparate etching depths, thereby
corroborating the uniform dispersion of Y7-BO throughout the BHJ film.
Conversely, a discernible attenuation in N 1 s signal intensity is observedin BHJ films incorporating additive contents of 0.1 vol% and 0.5 vol% as
the etching process advanced, indicative of a pronounced accumulationof Y7-BO predominantly at the film’s upper region. Consequently,
incorporating additives within BHJ films engenders a heightened vertical phase segregation propensity. This phase separation optimizes the
charge separation and transport mechanisms in phototransistors. The
vertical phase segregation enhances charge separation efficiency byconcentrating photon absorption and electron-hole pair generation in
the acceptor region, thereby reducing recombination probability [34].
Additionally, vertical phase separation minimizes donor-acceptor mixing within the same plane, reducing defects and trap states that typicallycause charge recombination and transport losses [35]. This leads tomore complete and continuous transport pathways, thereby enhancing
charge transport speed and efficiency [36,37].
Furthermore, the regional molecular ordering and orientation of Y7BO and Y7-BO/CN in films were tested by grazing incidence wide-angle

Fig. 2. (a-c) The AFM images of BHJ film without additives and with different additive contents of 0.1 vol% and 0.5 vol%. (d-f) DXPS spectra of BHJ film without
additives and with different additive contents of 0.1 vol% and 0.5 vol%. (g-h) 2D grazing incidence wide-angle X-ray scattering (GIWAXS) image of Y7-BO without
and with CN. (i) The absorption spectra of PM6, Y7-BO, and PM6/Y7-BO composite film.
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X-ray scattering (GIWAXS), as shown in Fig. 2 g and 2 h. The corresponding data is carried out and shown in Table S1. The Y7-BO neatfilms without CN on PS shows a π–π stacking peak at 1.734 Å− 1 in theout-of-plane (OOP) direction and major lamellae stacking peaks at
0.272 Å− 1 in the in-plane (IP) direction, which is assigned to (110)lattice plane, determined from single crystal data previously reported[38,39]. For Y7-BO with CN, Fig. 2 h obviously shows that the orientation of Y7-BO is enhanced after the addition of CN, and the stacking
peak in OOP and IP direction shows a slight position change compared
to pristine Y7-BO. The same phenomenon is observed in PM6/Y7-BO
films. As shown in Fig. S8, the stacking peaks in both IP and OOP directions increase after introducing CN compared with the film without

Fig. 3. (a) Transfer characteristics of the phototransistor without additives, captured under both dark conditions and illuminations with wavelengths of 450, 520,
650, and 830 nm. The incident light intensity is maintained at 1 mW/cm2. (b) PPC and NPC response behaviors exhibited by the phototransistor without additives
when exposed to light at wavelengths of 450, 520, 650, and 830 nm. (c) The statistical data of IDS response of additive-free phototransistors when subjected to
830 nm light illumination at varying reading voltages. (d-e) Continuous IDS responses of phototransistors with varied additive contents when stimulated by 830 nm
light pulses under reading voltages of 5 V and − 5 V, respectively (td = 0.5 s, Δt = 0.5 s, light intensity: 3.72 mW/cm2). (f-g) The statistical data of ΔIDS for thephototransistors with distinct additive contents, exposed to an 830 nm light pulses at an intensity of 1 mW/cm2, recorded at reading voltages of 5 V and − 5 V
respectively. (h) The statistical data of ΔIDS for the phototransistor with distinct additive contents, exposed to 450, 520, and 650 nm light at an intensity of 1 mW/
cm2, recorded at VR of 5 V. (i-j) Simulated light absorption enhancement distributions for the BHJ/pentacene composite film within devices based on the BHJ film,
both in its pristine form and with an additive content of 0.25 vol%.
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additives. These results indicate that more substantial and more orderly
crystallinity exists in Y7-BO with CN [40,41]. The absorption spectra in
Fig. 2i also illustrate the crystallization and aggregation characteristicsof PM6 and Y7-BO. For the BHJ film without additives, the characteristic
peak of PM6 at 630 nm exhibits a red shift compared to that in thepristine PM6 film at 625 nm. The characteristic peak of Y7-BO at
838 nm shows a blue shift relative to the pristine Y7-BO film at 880 nm,
substantiating the presence of intermolecular interactions between PM6
and Y7-BO [42]. The complementary absorption characteristics of PM6
and Y7-BO markedly enhance the device’s overall performance. PM6
synergizes with Y7-BO can absorb the whole visible and NIR light. Thispairing creates a synergistic effect that significantly boosts the phototransistor’s efficiency and sensitivity. The donor-acceptor combinationof PM6 and Y7-BO effectively mitigates defect issues commonly associated with traditional heterojunctions, resulting in improved charge
separation and transport efficiency [43,44]. This combination minimizes recombination losses and maximizes the photocurrent, leading toremarkable performance in phototransistor applications. Subsequently,
the phototransistor was constructed with a ternary heterostructure of
BHJ/pentacene. Fig. S9 and S10 investigate the morphology of pentacene deposited on BHJ. Initially, when a 2 nm layer of pentacene wasdeposited on BHJ, the pentacene grains exhibited a loose and disorderedstate in films without pores. However, with a gradual increase in thecontents of additives for BHJ film, the pentacene grains exhibited
continuous growth along the Y7-BO, specifically along the edges ofpores. When the pentacene thickness reaches 50 nm, the pentacene
grains on the BHJ films, without and with 0.1 vol% additives, are uniformly distributed and densely packed. However, with additive contentsof 0.25 vol%, 0.5 vol%, and 1 vol%, the pentacene grains infiltrated
numerous pores, resulting in an uneven morphology of the pentacene
film.
The electrical characteristics of the phototransistor are further
investigated. Fig. 3a and Fig. S11 illustrate the transfer curves of thedevice constructed from BHJ/pentacene without additives and withdifferent additive contents under both dark conditions and varying lightilluminations with different wavelengths. The device without additives
shows a typical p-type behavior and a current ratio of over 103 for dark
conditions. Under light illumination, the photoresponse transitions from
NPC to PPC by applying gate voltage. The signal reverses around VGS =4 V (defined as Vt), where the cross point is between the transfer characteristic curves under dark and illumination. The transformation ofphotoresponse characteristics under different gate reading voltages (VR)is shown in Fig. 3b. For the reading voltage of 5 V, the device’s photocurrent shows a positive response when the light is turned on. After thelight is turned off, the photocurrent decays to a new equilibrium statehigher than the initial resting state instead of disappearing completely,
thus manifesting non-volatile characteristics. On the contrary, Under thereading voltage of − 5 V, the device exhibits negative response andthreshold switching characteristics, with the peak current lower than theinitial resting state, and the photocurrent recovers quickly after the
stimulation is completed. Fig. 3c and Fig. S12 show the photocurrent
variation of the device at different reading voltages with the stimulation
under 830 nm light (1 mW/cm2, 0.5 s). As the reading voltage of positive and negative VGS increases, the variation in current becomes moresignificant. Therefore, in order to minimize the impact of electron programming from VGS on the current of the device, we selected a reading
voltage of ±5 V based on the signal reverses of current in the transfer
curve to further investigate PPC and NPC behavior. Additionally, thetransfer characteristics presented in Fig. S11 demonstrate that devices
with additive contents of 0.1 vol%, 0.25 vol%, and 0.5 vol% exhibit
both positive and negative current changes at different VGS when
exposed to light. When the additive content reaches 1 vol%, the transfer
curve shifts to the positive VGS direction after light illumination, due tothe excessive porosity affecting the continuity of the pentacene layer.
The distinct photocurrent observed at different VGS values is also supported by the typical output curve obtained under both dark and light

conditions. Fig. S13 and S14 demonstrate that for VGS < 0, the IDS underlight illumination was lower compared to the dark condition, indicatinga negative photoresponse. Conversely, for VGS > 0, the IDS under lightillumination surpasses that observed in the dark, exhibiting a positive
photoresponse. These results show that the polarity of the photoresponse of the device is affected by the reading voltage, which isconsistent with the results of the transfer characteristic curves.
The phenomenon of consecutive photoconductance was subsequently observed by stimulating with multiple light pulses. Figs. 3d and3e illustrate the photoresponse of devices based on BHJ/pentacene withvarying additive contents in BHJ film under consecutive 830 nm lightpulsed stimulation (3.72 mW/cm2, td = 0.5 s, Δt = 0.5 s). Similarly, at areading voltage of 5 V, an increase in light pulses results in a progressive
rise in IDS, suggesting consistent positive photoconductance. In contrast,
under a reading voltage of − 5 V, the device demonstrates a negative andvolatile photoresponse. Notably, with a progressive increase in lightpulses, substantial and sustained variations in photoconductance areabsent, elucidating a characteristic threshold-switching behavior. It issignificant to note that with an additive content of 1 vol%, all devices
exhibit a positive photoresponse attributed to the discontinuous growthof pentacene. Fig. 3f-g and Fig. S15 illustrate the ΔIDS relative to theresting state after applying an 830 nm light pulse with the intensity of
1 mW/cm2. Both PPC and NPC behaviors demonstrate the most significant ΔIDS at an additive content of 0.25 vol%. Fig. 3h and Fig. S16 show
the ΔIDS for devices with different additive contents under stimulationsof distinct visible light wavelengths (450, 520, and 650 nm) with theintensity of 1 mW/cm2. It also indicates that the device with 0.25 vol%additives exhibits the most significant ΔIDS when subjected to visible
light stimuli.
The observed variation in ΔIDS can be attributed to diverse lightharvesting efficiencies of external light arising from variations in pore
diameters and depths caused by different additive contents. It can befurther validated through FDTD simulation modeling. Figs. 3i and 3jshow the spatial profiles of the light absorption per unit volume withrespect to the xz-plane at 830 nm for the flat interface between BHJ andpentacene. When constructing the model, we set the diameter of thepore in the BHJ model to 320 nm and the depth to 22 nm, which isconsistent with the morphology of the 0.25 vol% additive BHJ films. The
ratio of the absorbed power per unit volume (Pabs) of the porous structure to the planar structure is equal to the ratio of the square of theaverage intensity of electric field (E) [45]. According to the simulation
results of Figs. 3i and 3j, the Pabs are increased by 2.5-fold by porous
structure. The spatial distributions of light absorption at 450, 520, and
650 nm are illustrated in Fig. S17. Similarly, relative to a flat interface,
the porous structure amplifies visible light absorption. Following this,the influence of varying pore sizes on light absorption density is similarly investigated. As illustrated in Fig. S18, under 830 nm light incidence, the absorption density with the pore diameter of 320 nm(0.25 vol%) was higher than that of 150 nm (0.1 vol%), consistent withthe results reflected in the device’s photocurrent.
To further clarify the positive and negative photoconductive behaviors observed under different reading voltages in the device, theenergy level of the phototransistor-based BHJ/pentacene is illustrated inFig. 4a. It indicates that pentacene forms a heterostructure with BHJ.
Therefore, under illumination conditions, electrons and holes generated
within the BHJ are simultaneously influenced by both the voltage
applied on the gate and the energy barrier of pentacene. When VGS > Vt,
upon absorption of external light by the BHJ, the photo-generated excitons diffuse to the D/A interface, and the energy offsets between PM6
and Y7-BO lead to dissociation of the strongly bonded Frenkel excitons[46,47]. Under the influence of the electric field, electrons migrate towards the gate and are ultimately trapped by the PS. The resultant
built-in electric field induces an accumulation of holes in the BHJ,
leading to an increase in current and exhibiting non-volatile characteristics (Fig. 4b). Conversely, When VGS < Vt, electron-hole pairs dissociate
at the D-A interface, with holes entering into the pentacene due to the5

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Fig. 4. (a) Schematic diagram of energy level diagram of the photonic synapse. The working mechanism of the device under (b) VR of 5 V with non-volatile PPC
behavior and (c) VR of − 5 V with volatile NPC behavior. (d) Schematic diagram of image edge detection process for day and night, respectively. (e) Schematic
principle of the phototransistor array to achieve image processing. (f) The edge detection of objects in both bright and dark conditions realized by the response ofphototransistors to visible and NIR light.

potential barrier energy levels. The excess electrons are left in the BHJ,
causing a decreasing current. Under the driving force of the gate voltage,
the holes quickly recombine with electrons within the BHJ. Consequently, the device manifests volatile properties (Fig. 4c).
The transition in the behaviors of PPC and NPC can be further substantiated by examining the individual charge transfer behaviors ofdevices based on PM6 and Y7-BO. The device comprising PM6/pentacene was then constructed, as shown in Fig. S19. The transfer curves
obtained under both dark conditions and illumination with 520 nm lightat an intensity of 1 mW/cm2 illustrate an elevated IDS within the device
during illumination as compared to its dark state, demonstrating a
positive photoresponse. This observation is attributed to the photogenerated electrons from PM6 being trapped by the PS, leaving excess
holes and increasing the current. Subsequently, devices incorporating
Y7-BO/pentacene were similarly constructed. As depicted in Fig. S20,
the transfer curves observed under dark and light illumination with
830 nm at an intensity of 1 mW/cm2 reveal that the device exhibits anegative photoresponse. This behavior arises from Y7-BO’s pronounced
electron-trapping capability. Consequently, holes generated under illumination transfer to pentacene due to potential energy barriers, leaving
excess electrons within the Y7-BO layer. The device comprised of PM6/
Y7-BO further substantiates the role of pentacene, as illustrated inFig. S21. The transfer curves under both dark conditions and light illumination with 520 and 830 nm reveal that the device consistently displays a positive photoresponse during illumination. These results

indicate that the NPC behavior of the device is attributable to the photogenerated holes from the light-absorbing layer transitioning into thepentacene layer.
Employing the gate-tunable bidirectional photoresponse of our device to both visible and NIR light, we conducted a comprehensive
simulation focusing on critical data processing, particularly highlightingedge detection, as depicted in Fig. 4d. The bidirectional modulation ofvisible light proves effective for detecting feature edges in bright environments. In contrast, bidirectional modulation of NIR light becomes
essential in dark nighttime settings. The resulting photocurrent corresponds to a convolutional kernel, characterized by a 3 × 3 matrix
configuration, as illustrated in Fig. 4e. The edge detection process systematically unfolds through the sequential convolutional interaction ofindividual input pixels with the kernel matrix. As demonstrated inFig. 4f, our device enables precise edge detection for feature objects
under both bright and dark conditions. Leveraging the device’s broad
spectral response and spatial gradient extraction enhances image information’s spatial resolution and fidelity in complex lighting environments. The precision can reach 85 % under bright conditions andmaintain a precision of 70 % even in dark conditions. Fig. S22 compares
the edge extraction capabilities of devices without additives to thosewith 0.25 vol% additives. The additive-based device exhibits superioredge feature extraction ability due to the wider range of light conductance variation induced by light harvesting in the porous structure. The
bidirectional photoresponse capabilities inherent to our device not only6

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underscore its intrinsic technological prowess but also facilitate theemulation and integration of edge computing preprocessing paradigms
within visual imagery. This establishes its potential efficacy for visual
data analytics and processing in varying lighting conditions.
Considering the device’s ability to manifest PPC and NPC behaviors
across diverse VGS conditions, this device emerges as a promising
candidate for critical data encryption. Consequently, a comprehensive
investigation is undertaken to investigate the device’s photoresponsive
characteristics under continuous light pulses (1 mW/cm2, td = 0.5 s, Δt= 0.5 s) at specific wavelengths of 450, 520, and 650 nm. As shown in
Fig. 5a, under a reading voltage of 5 V, the device led a positive photoresponse for all three wavelengths and maintained a higher photocurrent level after 50 consecutive light pulses. Conversely, Fig. 5b
exhibits the negative photocurrents of the device at a reading voltage of− 5 V. To systematically evaluate the device’s performance under
different light pulses, a statistical overview of the variations in electricalconductivity for each wavelength is presented, as shown in Fig. 5c.

Based on the conductance values observed under different pulses andvarying wavelengths, these values were quantified and utilized toenhance the security of visual information through RGB-based encryption. Specifically, conductance values were translated into pixel intensities and incorporated into grayscale images, embedding RGB noise
within the images as shown in Fig. 5d. The encryption process primarily
relies on the intensity of the light pulses rather than their specificwavelengths, ensuring that image information is obscured by the overallnoise intensity, thereby creating a more robust cryptographic layer.
Employing RGB light for encryption is crucial as it introduces a multidimensional noise pattern, significantly enhancing the complexity andsecurity of the encrypted image. By integrating noise that varies with
RGB intensities, the encryption becomes more intricate and less predictable. During encryption, with a VGS of 5 V, the phototransistor array
operates in PPC mode under visible light, encoding the image information based on light-induced conductance changes. This setup
strengthens the encryption mechanism by directly embedding noise into

Fig. 5. (a) PPC and (b) NPC characteristics of the phototransistor with 0.25 vol% additives under 450, 520, and 650 nm light pulses and different reading voltages(pulse number: 50, td: 0.5 s, Δt = 0.5 s, pulse intensity = 1 mW/cm2). (c) Statistics of the positive photoconductance as a function of pulse number under different
light pulses with wavelengths of 450, 520, and 650 nm. The light intensity was 1 mW/cm2, and the duration and interval were both 0.5 s. (d) The image is encrypted
and decrypted by the array by applying 50 light pulses of 450, 520, and 650 nm with VGS = ± 5 V, and the decrypted image is further classified and identified by the
CNN network. (e) A comparison of images encrypted and decrypted by the array. (f) The recognition accuracy of images encrypted and decrypted by the array.
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the visual information. For decryption, negative photoconductivity
values are utilized with a reading voltage of − 5 V. This negative photoconductance assists in reversing the encryption process by mappingthese values back onto the corresponding pixels in the image, thus
recovering the original image information. This decryption method
effectively compensates for the noise introduced during encryption. The
process was validated by encrypting 10 typical images from the Fashion
MNIST dataset using the phototransistor array, as demonstrated inFig. 5e. Using the conductance of 50 light pulses to encrypt the images,
the particularly fuzzy images were obtained. The convolutional neuralnetwork (CNN) was further used to recognize the encrypted image, andthe recognition accuracy was only 12 %, attesting to the effectiveness ofthe encryption process in concealing visual information. Fig. S23 illustrates the encryption strength of the image at three distinct levels basedon the number of light pulses. We extracted the conductance values at
10, 30, and 50 light pulses to encrypt the image. It can be seen that as thenumber of pulses increases, the encryption effect of the pattern is better.
It demonstrates that the phototransistor array can effectively serve an
image encryption function. In stark contrast, the decrypted images
demonstrated in Fig. 5e exhibit obvious original images with a recognition accuracy exceeding 90 %, affirming the device’s exceptional
capability in decryption operations. Fig. S24 and S25 depict thedecryption of the image at three distinct levels under varying light intensities. When the light intensity is reduced to 0.25 mW/cm2, thedecryption effect of the phototransistor array on the image is illustrated
in Fig. S24b. At this point, the decrypted image remains somewhat
blurred, with only a 52 % recognition accuracy (Fig. S24c). With a lightintensity of 0.5 mW/cm2, the decrypted image achieves a recognition
accuracy of 60 %. Fig. S26 exhibits the images encrypted and decryptedby the transistor without additives. However, devices without additives
can still encrypt and decrypt image information. It can be seen from
Fig. S27 that the device incorporating additives has better encryption
and decryption effects, attributed to a larger range of conductivity
changes. These results underscore the device’s improvement in strong
encryption and reliable decryption of visual information after introducing the porous structure.

with nitrogen. PS solution (5 mg/mL in toluene) was spun-coated onto
the Si substrate at 3000 rpm for 40 s and annealed at 100 ℃ for 30 min
in glovebox with N2 atmosphere. For the phototransistors based on
additive-free BHJ, the concentration of PM6 and Y7-BO solution was 7.5
and 9 mg/mL in CF. PM6:Y7-BO solutions at volume ratio of 1:1 werespun onto the PS layer at 2000 rpm for 40 s and annealed at 100 ℃ for
10 min. For the device based on BHJ with additives, the CN was added in
Y7-BO soluion with volume ratio of 0.1 vol%, 0.25 vol%, 0.5 vol%, and
1 vol% and DDO was added in PM6 solution with the same volume ratio.
And then two solutions were mixed in a volume ratio of 1:1 and spun
onto the PS layer at 2000 rpm for 40 s and annealed at 100 ℃ for
10 min. Finally, 40 nm pentacene and 40 nm Cu layer was then deposited as the top electrode under high vacuum (≈3 × 10− 4 Pa). All electrical measurements were conducted using a Keithley 2636B
semiconductor parameter analyzer under ambient conditions without
any packaging. For all the phototransistors, illumination was performed
with a xenon light Source.
4.3. Characterization
The morphologies of the BHJ and pentacene films were characterized
by AFM (Bruker’s Dimension Icon Atomic Force Microscopy). The
UV–vis absorption spectra were obtained using a LAMBDA 35. GIWAXS
measurement were carried out with a Xeuss 2.0 SAXS/WAXS laboratory
beamline using a Cu X-ray source (8.05 keV, 1.54 Å) and a Pilatus3R
300 K detector. The samples for GIWAXS measurements are fabricatedon silicon substrates using the same recipe for the devices.
4.4. 3D FDTD simulation for absorption profile calculation
Full wave electromagnetic simulation was performed using Lumerical FDTD simulation software (www.lumerical.com). The simulation
structure was based on measured results of the actual device via AFM.
The refractive indices of the mesoporous BHJ and BHJ/pentacene layers
were taken from spectroscopic ellipsometry measurements. The plane
wave source with a wavelength was impinged along the z-direction, andthe periodic boundary conditions for x-y axis and the perfectly matchedlayer for z-axis were imposed on the unit-cell structure. Then, theabsorbed power per unit volume (Pabs) was calculated using the relation

3. Conclusion
The ternary organic heterostructure phototransistor was constructed
for adaptive critical data processing. A three-dimensional porous
photosensitive layer induced by the phase separation process enhances
the light absorption power per unit volume by 2.5-fold. The device
demonstrates a gate-tunable bidirectional photoresponse of non-volatile
PPC and volatile NPC to visible and NIR light, enabling image edge
detection in varying lighting conditions with precision above 85 %. The
high-fidelity encryption and decryption processes were achieved byphototransistor array with superior accuracy of 12 %/90 %. This innovative ternary organic heterostructure device exemplifies efficient andcompact processing capabilities for critical data.

of P= − 12 ω|E|2 Im(ε)where ω is the angular frequency, E is the electric field, and Im(ε) is the imaginary part of the permittivity of perovskite
material.
4.5. Image edge detection by transistor array

Firstly, the image was converted to grayscale, where pixel values
were scaled to a range from 0 to 255 to standardize the intensity levels
for uniform processing. This grayscale transformation ensures that eachpixel’s intensity is appropriately represented for subsequent analysis.
Next, we configured the phototransistor array to perform image edge
detection by programming it to apply a 3 × 3 convolutional kernel
matrix. Specifically, we employed edge detection kernels such as theSobel operators, which are commonly used for identifying horizontal⎛⎞− 1 0 1and vertical edges. The Sobel operator matrices are⎝ − 2 0 2 ⎠ and− 1 0 1⎛⎞− 1 − 2 − 1⎝ 0
0
0 ⎠ for horizontal and vertical edge detection. The pho121
totransistor array was programmed to represent these kernel matrices.
The array was then used to process different pixel values from the input
grayscale image. Each phototransistor’s responsivity (R) was measured
under different gate reading voltage (− 5, − 10, 5, and 10 V), which reflects the convolution operation applied to the image. This measurement
corresponds to a weighted average operation on the input image, where

4. Methods
4.1. Material preparation
PM6 (99 %, Mn 45k Da, Mw 97k Da, PDI = 2.16) and Y7-BO (98 %)were purchased from Nanjing Zhiyan Technology Co., Ltd. 1-chloronaphthalene (CN), 1,10-decanediol (DDO), polystyrene (MW: 35 w)
and chloroform (CF) were purchased from Aladdin. Pentacene waspurchased from J&K Scientific.
4.2. Device fabrication and measurement
The Si substrate was cleaned by ultrasonic treatment in acetone,
ethyl alcohol and deionized water, for 10 min, respectively. Next, the Si
substrate was treated 10 min under UV/ozone environment after drying
8Nano Energy 130 (2024) 110133

H. Shao et al.

the weights are determined by the convolutional kernel matrix. The
output photocurrent thus represents the result of applying the 3 × 3
convolutional kernel to each pixel in the grayscale image, facilitating theedge detection process by highlighting regions of significant intensitychange.

Data availability

4.6. Image encryption and decryption

The project was supported by the National Key R&D Program ofChina (2021YFA0717900), the National Natural Science Foundation ofChina (12204248, 62288102, 22275098), the Project of State Key
Laboratory of Organic Electronics and Information Displays, Nanjing
University of Posts and Telecommunications (GZR2024010030), theJiangsu Funding Program for Excellent Postdoctoral Talent(2022ZB399), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX23_0283), the Innovation and Technology Fund (MHP/126/21) from the Innovation and Technology
Commission of Hong Kong SAR, China.

Data will be made available on request.
Acknowledgements

For image encryption, we employ a reading voltage of 5 V, whichcorresponds to the positive photoconductance state of the device.
Initially, the conductance values of the device are mapped after it has
been exposed to varying numbers of NIR light pulses. This mapping isbased on the principle that the conductance of the device changes in
response to the intensity and number of light pulses it receives, thereby
encoding information related to the original image. Once the conductance values are mapped, we introduce Gaussian noise into the original
image to encrypt it. This noise is proportionally added based on the
conductance values of the device stimulated by RGB light under
different numbers of pulses, relative to the initial conductance value ofthe device. Specifically, we calculate the ratios CR, CG, and CB, where:
ΔGG
ΔGB
R
CR = ΔGΔG0 , CG = ΔG0 , and CB = ΔG0 is the initial conductance value of the

Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.nanoen.2024.110133.

device. These ratios dictate the level of Gaussian noise added to theimage, effectively encoding the image with color-specific noise.
For image decryption, we use a reading voltage of − 5 V, whichcorresponds to the negative photoconductance state of the device. Under
this condition, the device’s conductance is influenced by the RGB lightintensities. We measure the ratio of the device’s conductance variation
after RGB light stimulation compared to the conductance variation when
exposed to 1 mW/cm2 of 830 nm light. This ratio serves as the denoising
factor for decrypting the image. By adjusting the light intensities, weachieve different levels of denoising, which allows for the recovery of
the original image based on the varying effects of light intensity on thedevice’s conductance.

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4.7. Image recognition based on CNN
The Convolutional Neural Network (CNN) utilized the Rectified
Linear Unit (ReLU) activation function for all convolutional and fully
connected layers, except for the final layer, where the Softmax function
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CRediT authorship contribution statement
He Shao: Writing – review & editing, Writing – original draft,
Investigation, Formal analysis. Yu Ji: Investigation, Formal analysis,
Data curation. Ruiheng Wang: Validation, Data curation. Jing Liu:
Formal analysis, Data curation. Yueqing Li: Formal analysis, Data
curation. Benxin Liu: Formal analysis, Data curation. Wen Li: Writing –review & editing, Resources, Funding acquisition. Yannan Xie: Writing– review & editing, Resources, Funding acquisition. Linghai Xie:
Writing – review & editing, Supervision, Investigation, Funding acquisition, Conceptualization. Johnny C. Ho: Writing – review & editing,
Project administration, Funding acquisition. Wei Huang: Project
administration, Funding acquisition. Haifeng Ling: Writing – review &editing, Supervision, Funding acquisition, Conceptualization.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
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He Shao works as a postdoctoral fellow in Materials Scienceand Engineering at the City University of Hong Kong. She
worked in the Institute of Advanced Materials (IAM), Nanjing
University of Posts & Telecommunications (NJUPT) in 2021.
She obtained her B.S. from the College of Physics, Jilin Normal
University in 2016. In 2021, she obtained her Ph.D. from Jilin
University. Her research interests focus on phototransistors,
memristors, and synaptic devices.

Linghai Xie received the Ph.D. degree from the Institute ofAdvanced Materials (IAM), Fudan University in 2006, and thenhe worked in Nanjing University of Posts and Telecommunications (NUPT). He is currently a professor of materials
chemistry and a director at the Center for Molecular Systems
and Organic Devices (CMSOD). His research interest includes
the molecular design of organic semiconductors by ecofriendly
procedures, and their applications in organic devices and supramolecular optoelectronics.

Johnny C. Ho is a Professor of Materials Science and Engineering at the City University of Hong Kong. He received his B.
S. degree in Chemical Engineering and his M.S. and Ph.D. degrees in Materials Science and Engineering from the Universityof California, Berkeley, in 2002, 2005, and 2009, respectively.
From 2009–2010, he was a postdoctoral research fellow in the
Nanoscale Synthesis and Characterization Group at Lawrence
Livermore National Laboratory. His research interests focus on
the synthesis, characterization, integration, and device applications of nanoscale materials for various technological applications, including nanoelectronics, sensors, and energy
harvesting.

Haifeng Ling is a professor at Nanjing University of Posts andTelecommunications (NJUPT). He received his Ph.D. in organic
electronics from NJUPT. Then he worked as a postdoctoral
fellow at the Hong Kong Polytechnic University and an Alexander von Humboldt postdoctoral researcher at the Max Planck
Institute for Polymer Research, respectively. In 2019, he joined
the Institute of Advanced Materials (IAM) at NJUPT. His
research interests focus on organic flexible neuromorphic
electronics.

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