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学者姓名：陈惠鹏
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Abstract ：
Flexible organic synaptic transistors (FOSTs) are crucial for neuromorphic computing due to their flexibility and biocompatibility, yet their mechanical stability under strain is underexplored. This study enhances FOST resilience by optimizing the neutral-axis alignment through layer thickness adjustments and incorporation of a polyimide layer, aligning the axis closer to the heterojunction interface. This strategy significantly reduces strain-induced defects, minimizing excitatory postsynaptic current (EPSC) degradation from 21.19% to 13.34% after 100 bending cycles. Optimized FOSTs demonstrate a remarkable pattern recognition accuracy of 90.4% after bending, significantly outperforming the 76.8% achieved by standard devices. These findings present a straightforward and effective approach to improve the mechanical stability and synaptic performance of FOSTs, advancing the development of durable bio-inspired computing systems.
Keyword ：
Accuracy Accuracy Bending Bending Films Films Flexible synaptic transistor Flexible synaptic transistor mechanical stability mechanical stability Neuromorphics Neuromorphics neutral axis neutral axis pattern recognition pattern recognition Pattern recognition Pattern recognition Performance evaluation Performance evaluation Strain Strain Substrates Substrates Thermal stability Thermal stability Transistors Transistors
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| GB/T 7714 | Ma, Xiao , Zhuang, Bingyong , Chen, Huipeng . Optimizing Neutral-Axis Alignment for Improved Stability and Synaptic Performance in Flexible Transistors [J]. | IEEE ELECTRON DEVICE LETTERS , 2025 , 46 (3) : 444-447 . |
| MLA | Ma, Xiao 等. "Optimizing Neutral-Axis Alignment for Improved Stability and Synaptic Performance in Flexible Transistors" . | IEEE ELECTRON DEVICE LETTERS 46 . 3 (2025) : 444-447 . |
| APA | Ma, Xiao , Zhuang, Bingyong , Chen, Huipeng . Optimizing Neutral-Axis Alignment for Improved Stability and Synaptic Performance in Flexible Transistors . | IEEE ELECTRON DEVICE LETTERS , 2025 , 46 (3) , 444-447 . |
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Vibration signals from buildings are crucial for analysis, safety prediction, and early warnings. However, acquiring and analyzing these signals requires complex systems including sensor systems, storage devices, and computing equipment. All the part of the system rely on external power. This poses a challenge for buildings where the installation of complex equipment and power systems is inconvenient. This study proposes a self- powered, high-speed, and highly sensitive vibration detection system. It integrates a triboelectric nano- generator (TENG) and an organic field-effect synaptic transistor. A synaptic transistor with analog biomimetic synapse characteristics is proposed. The TENG and synaptic transistor's working principles and carrier transport characteristics are studied. Using TENG's output and the synaptic device's memory, the system detects and evaluates building vibration signals. The system's adaptability to one-dimensional signals allows for vibration classification and recognition using 1D-CNN, achieving 88.9% accuracy. This innovative strategy has broad prospects for solving vibration detection problems in special buildings and achieving lightweight, real-time, and intelligent monitoring.
Keyword ：
Building vibration identification System Building vibration identification System Self-powered Self-powered Synaptic transistor Synaptic transistor Triboelectric nanogenerator Triboelectric nanogenerator
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| GB/T 7714 | Guo, Xiao , Fan, Yuyang , Liu, Di et al. Vibration sensing system integrating triboelectric nanogenerator and synaptic transistor for self-powered building vibration identification [J]. | MEASUREMENT , 2025 , 249 . |
| MLA | Guo, Xiao et al. "Vibration sensing system integrating triboelectric nanogenerator and synaptic transistor for self-powered building vibration identification" . | MEASUREMENT 249 (2025) . |
| APA | Guo, Xiao , Fan, Yuyang , Liu, Di , Chen, Huipeng . Vibration sensing system integrating triboelectric nanogenerator and synaptic transistor for self-powered building vibration identification . | MEASUREMENT , 2025 , 249 . |
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随着数字化进程的持续推进，光信号处理的复杂度急剧攀升。在单一器件上实现对光信号的多功能、可切换处理，已成为一项重要的研究目标。基于此，我们成功研发出一种基于手性有机小分子材料的光电二极管器件。该器件能够在负电压或零电压条件下高精度地区分不同旋向的圆偏振光，表现出优异的圆偏振光探测性能。当切换至正偏压状态时，器件在界面层出现电荷累积现象，使得界面电导率发生变化，从而产生突触效应。该器件的双模式切换特性突破了传统光电器件的功能单一性限制，其创新设计结合了手性材料的光电协同调控与突触仿生机制，为光通信、智能感知与神经形态计算等领域提供了多功能集成化的解决方案，展现出推动新一代光电技术发展的潜力。
Keyword ：
光电二极管 光电二极管 光电突触 光电突触 光通信 光通信 圆偏振光 圆偏振光 手性有机小分子 手性有机小分子
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| GB/T 7714 | 黄伟龙 , 陈惠鹏 . 基于手性有机小分子材料的圆偏振光电突触二极管器件 [J]. | 功能材料与器件学报 , 2025 , 31 (02) : 113-120 . |
| MLA | 黄伟龙 et al. "基于手性有机小分子材料的圆偏振光电突触二极管器件" . | 功能材料与器件学报 31 . 02 (2025) : 113-120 . |
| APA | 黄伟龙 , 陈惠鹏 . 基于手性有机小分子材料的圆偏振光电突触二极管器件 . | 功能材料与器件学报 , 2025 , 31 (02) , 113-120 . |
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Reliably discerning real human faces from fake ones, known as antispoofing, is crucial for facial recognition systems. While neuromorphic systems offer integrated sensing-memory-processing functions, they still struggle with efficient antispoofing techniques. Here we introduce a neuromorphic facial recognition system incorporating multidimensional deep ultraviolet (DUV) optoelectronic synapses to address these challenges. To overcome the complexity and high cost of producing DUV synapses using traditional wide-bandgap semiconductors, we developed a low-temperature (<= 70 degrees C) solution process for fabricating DUV synapses based on PEA(2)PbBr(4)/C8-BTBT heterojunction field-effect transistors. This method enables the large-scale (4-in.), uniform, and transparent production of DUV synapses. These devices respond to both DUV and visible light, showing multidimensional features. Leveraging the unique ability of the multidimensional DUV synapse (MDUVS) to discriminate real human skin from artificial materials, we have achieved robust neuromorphic facial recognition with antispoofing capability, successfully identifying genuine human faces with an accuracy exceeding 92%.
Keyword ：
antispoofing antispoofing DUV synapses DUV synapses facial recognition system facial recognition system field-effect transistors field-effect transistors organic/perovskiteheterojunctions organic/perovskiteheterojunctions
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| GB/T 7714 | Xia, Jiangnan , Gao, Changsong , Peng, Chengyuan et al. Multidimensional Deep Ultraviolet (DUV) Synapses Based on Organic/Perovskite Semiconductor Heterojunction Transistors for Antispoofing Facial Recognition Systems [J]. | NANO LETTERS , 2024 , 24 (22) : 6673-6682 . |
| MLA | Xia, Jiangnan et al. "Multidimensional Deep Ultraviolet (DUV) Synapses Based on Organic/Perovskite Semiconductor Heterojunction Transistors for Antispoofing Facial Recognition Systems" . | NANO LETTERS 24 . 22 (2024) : 6673-6682 . |
| APA | Xia, Jiangnan , Gao, Changsong , Peng, Chengyuan , Liu, Yu , Chen, Ping-An , Wei, Huan et al. Multidimensional Deep Ultraviolet (DUV) Synapses Based on Organic/Perovskite Semiconductor Heterojunction Transistors for Antispoofing Facial Recognition Systems . | NANO LETTERS , 2024 , 24 (22) , 6673-6682 . |
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Neuromorphic visual systems can emulate biological retinal systems to perceive visual information under different levels of illumination, making them have considerable potential for future intelligent vehicles and vision automation. However, the complex circuits and high operating voltages of conventional artificial vision systems present great challenges for device integration and power consumption. Here, bioinspired synaptic transistors based on organic single crystal phototransistors are reported, which exhibit excitation and inhibition synaptic plasticity with time-varying. By manipulating the charge dynamics of the trapping centers of organic crystal-electret vertical stacks, organic transistors can operate below 1 V with record high on/off ratios close to 108 and sharp switching with a subthreshold swing of 59.8 mV dec-1. Moreover, the approach offers visual adaptation with highly localized modulation and over 98.2% recognition accuracy under different illumination levels. These bioinspired visual adaptation transistors offer great potential for simplifying the circuitry of artificial vision systems and will contribute to the development of machine vision applications. A bioinspired synaptic transistor based on organic crystal-electret stacks is developed, which presents visual adaption with highly localized modulation and over 98.2% recognition accuracy. By manipulating the charge dynamics of the trapping centers, organic transistors can operate below 1 V with record high on/off ratio close to 108 and sharp switching of a subthreshold swing of 59.8 mV dec-1. image
Keyword ：
organic field-effect transistor organic field-effect transistor steep switching steep switching ultralow voltage ultralow voltage visual adaption visual adaption
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| GB/T 7714 | Duan, Shuming , Zhang, Xianghong , Xi, Yue et al. Solution-Processed Ultralow Voltage Organic Transistors With Sharp Switching for Adaptive Visual Perception [J]. | ADVANCED MATERIALS , 2024 , 36 (32) . |
| MLA | Duan, Shuming et al. "Solution-Processed Ultralow Voltage Organic Transistors With Sharp Switching for Adaptive Visual Perception" . | ADVANCED MATERIALS 36 . 32 (2024) . |
| APA | Duan, Shuming , Zhang, Xianghong , Xi, Yue , Liu, Di , Zhang, Xiaotao , Li, Chunlei et al. Solution-Processed Ultralow Voltage Organic Transistors With Sharp Switching for Adaptive Visual Perception . | ADVANCED MATERIALS , 2024 , 36 (32) . |
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Efficient in-sensor computing necessitates linear, bidirectional, and centrosymmetric photoresponse weight updates; however, the realization of these attributes poses a persistent challenge, with most photosensor devices achieving linear analog weight updates while falling short of accomplishing bidirectional and centrosymmetric characteristics. Here, the development of a quantum dot (QD)-based bulk heterojunction synaptic transistor (QBST) with multi-factor modulation through surface ligand engineering of blend QDs is reported. By controlling the charge transmission between QDs and the semiconductor, the QBST device enables tunable fading memory, which transforms linear weight updates in short-chain devices into linear, bidirectional, and unprecedented centrosymmetric optical synaptic responses in long-chain devices. Moreover, through the synergy of chemical and electric factors, the convolutional kernel of QBSTs-based convolutional neural network realizes enhanced recognition for complex noisy fashion-costume images, achieving an impressive 90.3% accuracy in the long-chain device, highlighting the efficiency of centrosymmetric weight updates. The results demonstrate that surface ligand engineering offers a promising approach for customizable synaptic modulation, facilitating energy- and time-efficient in-sensor computing. By modulating the ligand chain length of perovskite QDs, bulk heterojunction synaptic transistors can achieve multi-factor optical synaptic modulation, enabling tunable fading memory. Notably, the optical synaptic weight transforms linear weight updates in short-chain devices into linear, bidirectional, and unprecedented centrosymmetric optical synaptic responses in long-chain devices, showcasing their tremendous potential in high-accuracy in-sensor computing applications. image
Keyword ：
bulk heterojunction bulk heterojunction in-sensor computing in-sensor computing multi-factor modulation multi-factor modulation organic synaptic transistor organic synaptic transistor quantum dot ligand engineering quantum dot ligand engineering
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| GB/T 7714 | Li, Enlong , Wang, Xiumei , Yu, Xipeng et al. Multi-Factor Modulated Organic Bulk Heterojunction Synaptic Transistor Enabled by Ligand Engineering for Centrosymmetric In-Sensor Computing [J]. | ADVANCED FUNCTIONAL MATERIALS , 2024 , 34 (26) . |
| MLA | Li, Enlong et al. "Multi-Factor Modulated Organic Bulk Heterojunction Synaptic Transistor Enabled by Ligand Engineering for Centrosymmetric In-Sensor Computing" . | ADVANCED FUNCTIONAL MATERIALS 34 . 26 (2024) . |
| APA | Li, Enlong , Wang, Xiumei , Yu, Xipeng , Yu, Rengjian , Li, Wenwu , Guo, Tailiang et al. Multi-Factor Modulated Organic Bulk Heterojunction Synaptic Transistor Enabled by Ligand Engineering for Centrosymmetric In-Sensor Computing . | ADVANCED FUNCTIONAL MATERIALS , 2024 , 34 (26) . |
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Deep neural networks have revolutionized several domains, including autonomous driving, cancer detection, and drug design, and are the foundation for massive artificial intelligence models. However, hardware neural network reports still mainly focus on shallow networks (2 to 5 layers). Implementing deep neural networks in hardware is challenging due to the layer-by-layer structure, resulting in long training times, signal interference, and low accuracy due to gradient explosion/vanishing. Here, we utilize negative ultraviolet photoconductive light-emitting memristors with intrinsic parallelism and hardware-software co-design to achieve electrical information's optical cross-layer transmission. We propose a hybrid ultra-deep photoelectric neural network and an ultra-deep super-resolution reconstruction neural network using light-emitting memristors and cross-layer block, expanding the networks to 54 and 135 layers, respectively. Further, two networks enable transfer learning, approaching or surpassing software-designed networks in multi-dataset recognition and high-resolution restoration tasks. These proposed strategies show great potential for high-precision multifunctional hardware neural networks and edge artificial intelligence. Parallel information transmission components and hardware strategies are still lacking in neural networks. Here, the authors propose a strategy to use light emitting memristors with negative ultraviolet photoconductivity and intrinsic parallelism to construct direct information cross-layer modules.
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| GB/T 7714 | Chen, Zhenjia , Lin, Zhenyuan , Yang, Ji et al. Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability [J]. | NATURE COMMUNICATIONS , 2024 , 15 (1) . |
| MLA | Chen, Zhenjia et al. "Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability" . | NATURE COMMUNICATIONS 15 . 1 (2024) . |
| APA | Chen, Zhenjia , Lin, Zhenyuan , Yang, Ji , Chen, Cong , Liu, Di , Shan, Liuting et al. Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability . | NATURE COMMUNICATIONS , 2024 , 15 (1) . |
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Artificial photonic synapses offer an efficient solution for overcoming the von Neumann bottleneck in data storage and processing, providing advantages over electrical synapses by eliminating the bandwidth-connection-density tradeoff and exhibiting low power consumption. Perovskite quantum dots (QDs) have garnered significant attention in artificial photonic synapses due to their facile synthesis and favorable optoelectronic properties. However, challenges such as limited carrier mobility and nonlinearity impede their performance in neuromorphic applications. In this study, CsPbBr3-attached MXene nanostructures (CsPbBr3-MXene), in-situ growth of CsPbBr3 QDs on MXene nanosheets, were proposed as the light-absorbing layer of a synaptic phototransistor. The heterostructure formed by CsPbBr3 and MXene enhances photocurrent generation. Comparative analyses between CsPbBr3-MXene synapse transistor and that containing only CsPbBr3 revealed a 24.6% higher excitatory postsynaptic current (EPSC) in the CsPbBr3-MXene one under identical light pulse stimulation. Following calculations and comparisons, the linearity exhibited significant improvement, decreasing from 4.586 to 1.099. Furthermore, the recognition accuracy in handwritten digit classification notably increased, rising from 86.13% to 92.05%. Moreover, the F1 score in edge detection had improvement, advancing from 0.8165 to 0.9065, approaching closer to 1. These improvements have demonstrated substantial assistance in the field of neural computing.
Keyword ：
CsPbBr3-attached MXene CsPbBr3-attached MXene image preprocessing image preprocessing in-situ growth in-situ growth pattern recognition accuracy pattern recognition accuracy synaptic phototransistor synaptic phototransistor
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| GB/T 7714 | Dai, Yan , Chen, Gengxu , Huang, Weilong et al. A high-linearity synaptic phototransistor based on CsPbBr3-attached MXene nanostructures for image classification and edge detection tasks [J]. | SCIENCE CHINA-MATERIALS , 2024 , 67 (7) : 2246-2255 . |
| MLA | Dai, Yan et al. "A high-linearity synaptic phototransistor based on CsPbBr3-attached MXene nanostructures for image classification and edge detection tasks" . | SCIENCE CHINA-MATERIALS 67 . 7 (2024) : 2246-2255 . |
| APA | Dai, Yan , Chen, Gengxu , Huang, Weilong , Xu, Chenhui , Liu, Changfei , Huang, Ziyu et al. A high-linearity synaptic phototransistor based on CsPbBr3-attached MXene nanostructures for image classification and edge detection tasks . | SCIENCE CHINA-MATERIALS , 2024 , 67 (7) , 2246-2255 . |
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Artificial sensory systems require appropriate input-output modalities to enhance interaction efficiency and accuracy. It is crucial to mimic an organism to perceive and integrate multiple external stimuli and to construct systems with multiple sensory inputs. In addition, the application of optical output to the sensory system not only solves the problem of electrical signal transmission, but also enables direct imaging through optical output. Here, we propose an artificial multisensory system with optical feedback (AMSOF), which can simultaneously receive pressure and thermal stimuli and then generate neuromorphic optical responses along with feedback. Among them, the triboelectric nanogenerator (TENG) is responsible for receiving pressure stimuli, while the artificial light-emitting synaptic device utilizes the quantum dot thermal quenching mechanism to processes temperature signals and generate optical feedback. Additionally, if the AMOFS is applied to the fingerprint recognition system, it can enable fingerprint imaging through dual recognition of these two signals. In other words, it can enhance the security of biometric authentication by adding the capability to distinguish the live nature of objects on top of traditional fingerprint recognition. Therefore, this system aims to achieve more intelligent and efficient human-computer interaction and environmental perception capabilities, bringing forth a multitude of applications and innovations for smart systems.
Keyword ：
Artificial light -emitting synapse Artificial light -emitting synapse Human -machine interaction Human -machine interaction QLED device QLED device Triboelectric nanogenerator Triboelectric nanogenerator
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| GB/T 7714 | Chen, Huimei , Shan, Liuting , Gao, Changsong et al. Artificial multisensory system with optical feedback for multimodal perceptual imaging [J]. | CHEMICAL ENGINEERING JOURNAL , 2024 , 487 . |
| MLA | Chen, Huimei et al. "Artificial multisensory system with optical feedback for multimodal perceptual imaging" . | CHEMICAL ENGINEERING JOURNAL 487 (2024) . |
| APA | Chen, Huimei , Shan, Liuting , Gao, Changsong , Chen, Cong , Liu, Di , Chen, Huipeng et al. Artificial multisensory system with optical feedback for multimodal perceptual imaging . | CHEMICAL ENGINEERING JOURNAL , 2024 , 487 . |
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Signal processing has entered the era of big data, and improving processing efficiency becomes crucial. Traditional computing architectures face computational efficiency limitations due to the separation of storage and computation. Array circuits based on multi-conductor devices enable full hardware convolutional neural networks (CNNs), which hold great potential to improve computational efficiency. However, when processing large-scale convolutional computations, there is still a significant amount of device redundancy, resulting in low computational power consumption and high computational costs. Here, we innovatively propose a memristor-based in-situ convolutional strategy, which uses the dynamic changes in the conductive wire, doping area, and polarization area of memristors as the process of convolutional operations, and uses the time required for conductance switching of a single device as the computation result, embodying convolutional computation through the unique spiked digital signal of the memristor. Our strategy reasonably encodes complex analog signals into simple digital signals through a memristor, completing the convolutional computation at the device level, which is essential for complex signal processing and computational efficiency improvement. Based on the implementation of device-level convolutional computing, we have achieved feature recognition and noise filtering for braille signals. We believe that our successful implementation of convolutional computing at the device level will promote the construction of complex CNNs with large-scale convolutional computing capabilities, bringing innovation and development to the field of neuromorphic computing.
Keyword ：
conductive filaments conductive filaments convolutional computing convolutional computing memristor memristor multi-conductor multi-conductor
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| GB/T 7714 | Zhang, Xianghong , Qin, Congyao , Peng, Wenhong et al. Memristor-based in-situ convolutional strategy for accurate braille recognition [J]. | SCIENCE CHINA-MATERIALS , 2024 , 67 (12) : 3986-3993 . |
| MLA | Zhang, Xianghong et al. "Memristor-based in-situ convolutional strategy for accurate braille recognition" . | SCIENCE CHINA-MATERIALS 67 . 12 (2024) : 3986-3993 . |
| APA | Zhang, Xianghong , Qin, Congyao , Peng, Wenhong , Qin, Ningpu , Cheng, Enping , Wu, Jianxin et al. Memristor-based in-situ convolutional strategy for accurate braille recognition . | SCIENCE CHINA-MATERIALS , 2024 , 67 (12) , 3986-3993 . |
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