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学者姓名：陈惠鹏
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Abstract ：
Neuromorphic computing provides a promising solution to the von Neumann bottleneck and has received a lot of research attention. However, due to the limitation of static threshold activation of traditional neuromorphic devices, it is difficult to simulate the dynamic sparsity characteristics of biological neuron, resulting in more than 90% computational redundancy in fully connected neural networks architectures based on classical devices, which has become a key issue for efficient neuromorphic computing. Here, a dynamically adaptive activation neuron-transistor based on asymmetric electrodes and indium gallium zinc oxide thin films is proposed, overcoming the static activation limitations of conventional neuromorphic devices. The device achieves a dynamic adaptive activation similar to biometric neuron via gate voltage or UV irradiation, achieving a wide range of activation times (65 ms-13.5 s) and adjustable activation thresholds (2.5-7.7 V). Leveraging this device, a dynamic sparse spiking neural network (DS-SNN) is constructed that enables in situ Hadamard-based weight pruning/regeneration. Applied to autonomous driving object detection, the DS-SNN achieves 85% accuracy with 42% sparsity, outperforming dense convolutional/spiking neural networks (320k /140k weights) while utilizing only approximate to 80k parameters. This hardware-algorithm co-design establishes a new paradigm for energy-efficient edge-computing electronics, exploring 3D integration of large-scale neuromorphic processors.
Keyword ：
adaptive activation adaptive activation artificial neuron artificial neuron field effect transistor field effect transistor intelligent driver assistance intelligent driver assistance neuromorphic computing neuromorphic computing sparse spiking neural network sparse spiking neural network
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| GB/T 7714 | Gao, Changsong , Liu, Mingqiang , Aierken, Abuduwayiti et al. A Dynamic Adaptive Activation Neuron-Transistor for Dynamic Sparse Neural Networks in Advanced Driving Assistance System [J]. | ADVANCED FUNCTIONAL MATERIALS , 2025 . |
| MLA | Gao, Changsong et al. "A Dynamic Adaptive Activation Neuron-Transistor for Dynamic Sparse Neural Networks in Advanced Driving Assistance System" . | ADVANCED FUNCTIONAL MATERIALS (2025) . |
| APA | Gao, Changsong , Liu, Mingqiang , Aierken, Abuduwayiti , Liu, Xuefei , Wang, Degui , Wang, Zhen et al. A Dynamic Adaptive Activation Neuron-Transistor for Dynamic Sparse Neural Networks in Advanced Driving Assistance System . | ADVANCED FUNCTIONAL MATERIALS , 2025 . |
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The hierarchical processing capabilities of the human visual system can significantly enhance the efficiency of data processing in the central nervous system. Volatile and non-volatile devices are key components in simulating the central nervous system. Realizing both volatile and non-volatile functionalities on a single device is ideal; however, challenges such as complex preparation and cumbersome switching persist. In this study, a tunable synaptic transistor with volatile and non-volatile switching capabilities is developed, offering ease of fabrication and convenient switching. It can simulate various forms of synaptic plasticity and exhibits excellent storage performance in non-volatile mode. Finally, we design an image preprocessing and classification system based on visual selective attention, which enables efficient neuromorphic computation through hierarchical data processing.
Keyword ：
artificial synapse artificial synapse atomic layer deposition atomic layer deposition Data processing Data processing Electrons Electrons II-VI semiconductor materials II-VI semiconductor materials Logic gates Logic gates multilevel storage multilevel storage Multimode switching Multimode switching Nonvolatile memory Nonvolatile memory Switching circuits Switching circuits Transistors Transistors Visualization Visualization Voltage Voltage Zinc oxide Zinc oxide
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| GB/T 7714 | Zhang, Guocheng , Wang, Hongyu , Qin, Shixian et al. Tunable Synaptic Transistor With Volatile and Non-Volatile Switching Capabilities for Hierarchical Data Processing [J]. | IEEE ELECTRON DEVICE LETTERS , 2025 , 46 (5) : 789-792 . |
| MLA | Zhang, Guocheng et al. "Tunable Synaptic Transistor With Volatile and Non-Volatile Switching Capabilities for Hierarchical Data Processing" . | IEEE ELECTRON DEVICE LETTERS 46 . 5 (2025) : 789-792 . |
| APA | Zhang, Guocheng , Wang, Hongyu , Qin, Shixian , Tang, Jianchuan , Zeng, Zili , Su, Changqiang et al. Tunable Synaptic Transistor With Volatile and Non-Volatile Switching Capabilities for Hierarchical Data Processing . | IEEE ELECTRON DEVICE LETTERS , 2025 , 46 (5) , 789-792 . |
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Convolutional neural network (CNN) is currently one of the most important artificial neural networks. However, traditional CNN hardware architectures suffer from significant increases in energy consumption and processing time as the demand for artificial intelligence tasks grows. Here, a novel optical convolution computing strategy is proposed that leverages a continuously adjustable photoluminescent device (CA-PLD) as the optical convolution kernel, enabling parallel, all-optical convolution computing and greatly simplifying the traditional convolution process. Under ultraviolet illumination, the CA-PLD exhibits visible long-afterglow emission characteristics due to the charge trapping and retention effects. This allows for continuously adjustable light weights, facilitating arbitrary convolution operations. Building on this, parallel and efficient multiply-accumulate operations have been successfully demonstrated using CA-PLD arrays with different weight combinations. Moreover, space-transformable CA-PLD units enable applications in dilated convolution. In a semantic segmentation task with 20 categories, the CA-PLD units achieve higher Intersection over Union (IoU) values and accuracy. Therefore, the weight-adjustable and spatial transformable CA-PLD proposed in this work holds promise for future applications in intelligent optical computing systems and optical implementations of non-von Neumann architectures.
Keyword ：
All-optical convolution computing All-optical convolution computing long-afterglow emission long-afterglow emission non-von Neumann architecture non-von Neumann architecture photoluminescent device photoluminescent device
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| GB/T 7714 | Shan, Liuting , Xu, Chenhui , Pan, Jianyong et al. A Simple Optical Convolution Strategy Based on Versatile Adjustable Optical Convolution Kernel for All-Optical Convolution Computing [J]. | ADVANCED MATERIALS , 2025 , 37 (27) . |
| MLA | Shan, Liuting et al. "A Simple Optical Convolution Strategy Based on Versatile Adjustable Optical Convolution Kernel for All-Optical Convolution Computing" . | ADVANCED MATERIALS 37 . 27 (2025) . |
| APA | Shan, Liuting , Xu, Chenhui , Pan, Jianyong , Lu, Wenjie , Ma, Xiao , Liu, Di et al. A Simple Optical Convolution Strategy Based on Versatile Adjustable Optical Convolution Kernel for All-Optical Convolution Computing . | ADVANCED MATERIALS , 2025 , 37 (27) . |
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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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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 et al. "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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Inspired by the human retina, the development of neuromorphic vision systems featuring image perception, memory, and processing functions aims to address the limitations of traditional artificial vision systems concerning circuit simplification, device integration, and power consumption. The narrow spectral response of optoelectronic neurons, an important hardware basis for neuromorphic vision systems, limits their application in broad-spectrum artificial visual perception. In this study, we present an artificial optoelectronic neuron that demonstrates broadband sensing capabilities with a response range encompassing ultraviolet, visible, and near-infrared regions. Furthermore, we have designed a 64 x 64 array of optoelectronic neurons capable of effectively simulating the light perception and image pre-processing functions (enhance image contrast), of the retina. This work is important for improving image processing efficiency and realizing neuromorphic vision systems with broadband perception.
Keyword ：
broadband sens ing broadband sens ing image pre-processing image pre-processing neuromorphic visual system neuromorphic visual system Optoelectronic neurons Optoelectronic neurons
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| GB/T 7714 | Zhang, Guocheng , Tang, Jianchuan , Lai, Binglin et al. Retina-Inspired Artificial Optoelectronic Neurons With Broad Spectral Response for Visual Image Pre-Processing [J]. | IEEE ELECTRON DEVICE LETTERS , 2025 , 46 (3) : 401-404 . |
| MLA | Zhang, Guocheng et al. "Retina-Inspired Artificial Optoelectronic Neurons With Broad Spectral Response for Visual Image Pre-Processing" . | IEEE ELECTRON DEVICE LETTERS 46 . 3 (2025) : 401-404 . |
| APA | Zhang, Guocheng , Tang, Jianchuan , Lai, Binglin , Wang, Hongyu , Zeng, Zili , Su, Changqiang et al. Retina-Inspired Artificial Optoelectronic Neurons With Broad Spectral Response for Visual Image Pre-Processing . | IEEE ELECTRON DEVICE LETTERS , 2025 , 46 (3) , 401-404 . |
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Artificial intelligence is developing towards multimodal perception, and display technology is evolving into intelligent human-computer interaction. Owing to the intuitive and anti-interference advantages of optical outputs, it is essential to apply them to artificial multisensory systems. Herein, we propose a multimodal perception system with optical feedback that utilizes an integrated triboelectric nanogenerator (TENG) in conjunction with a quantum dot light-emitting synaptic device (QLESD), where TENG serves as a receiver for pressure signals and QLESD functions as both ultraviolet (UV) light and temperature receptor. Three distinct signals were memorized and processed in QLESD, which ultimately outputs light and electrical signals that combined these three stimuli. The excitatory postsynaptic current (EPSC) and EP brightness (EPSB) of QLESD stimulated by pressure signal from TENG were systematically investigated. Notably, EPSC and EPSB of the QLESD were enhanced with increasing contact frequency. Furthermore, as both the temperature and UV light intensity increased gradually, the suppression effect on synaptic signal transmission and memory became more pronounced. The successfully integration of temperature and UV light in collaborative modulation of pressure signals has been achieved, showcasing remarkable potential applications in robotics and human-computer interaction.
Keyword ：
light-emitting diode light-emitting diode multimodal perception multimodal perception organic synaptic device organic synaptic device triboelectric nanogenerator triboelectric nanogenerator
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| GB/T 7714 | Lan, Shuqiong , Chen, Huimei , Chen, Huipeng . Multimodal perception system with optical feedback based on triboelectric nanogenerator and quantum dot light-emitting synaptic device [J]. | JOURNAL OF PHYSICS D-APPLIED PHYSICS , 2025 , 58 (22) . |
| MLA | Lan, Shuqiong et al. "Multimodal perception system with optical feedback based on triboelectric nanogenerator and quantum dot light-emitting synaptic device" . | JOURNAL OF PHYSICS D-APPLIED PHYSICS 58 . 22 (2025) . |
| APA | Lan, Shuqiong , Chen, Huimei , Chen, Huipeng . Multimodal perception system with optical feedback based on triboelectric nanogenerator and quantum dot light-emitting synaptic device . | JOURNAL OF PHYSICS D-APPLIED PHYSICS , 2025 , 58 (22) . |
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The combination of artificial neural networks (ANN) and spiking neural networks (SNN) holds great promise for advancing artificial general intelligence (AGI). However, the reported ANN and SNN computational architectures are independent and require a large number of auxiliary circuits and external algorithms for fusion training. Here, a novel vertical bulk heterojunction neuromorphic transistor (VHNT) capable of emulating both ANN and SNN computational functions is presented. TaOx-based electrochemical reactions and PDVT-10/N2200-based bulk heterojunctions are used to realize spike coding and voltage coding, respectively. Notably, the device exhibits remarkable efficiency, consuming a mere 0.84 nJ of energy consumption for a single multiply accumulate (MAC) operation with excellent linearity. Moreover, the device can be switched to spiking neuron and self-activation neuron by simply changing the programming without auxiliary circuits. Finally, the VHNT-based artificial spiking neural network (ASNN) fusion simulation architecture is demonstrated, achieving 95% accuracy for Canadian-Institute-For-Advanced-ResearchResearch-10 (CIFARResearch-10) dataset while significantly enhancing training speed and efficiency. This work proposes a novel device strategy for developing high-performance, low-power, and environmentally adaptive AGI.
Keyword ：
artificial spiking neural network artificial spiking neural network bulk heterojunctions bulk heterojunctions electrochemical reactions electrochemical reactions
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| GB/T 7714 | Zou, Yi , Liu, Di , Gan, Xinyan et al. Toward Switching and Fusing Neuromorphic Computing: Vertical Bulk Heterojunction Transistors with Multi-Neuromorphic Functions for Efficient Deep Learning [J]. | ADVANCED MATERIALS , 2025 , 37 (27) . |
| MLA | Zou, Yi et al. "Toward Switching and Fusing Neuromorphic Computing: Vertical Bulk Heterojunction Transistors with Multi-Neuromorphic Functions for Efficient Deep Learning" . | ADVANCED MATERIALS 37 . 27 (2025) . |
| APA | Zou, Yi , Liu, Di , Gan, Xinyan , Yu, Rengjian , Zhang, Xianghong , Gao, Chansong et al. Toward Switching and Fusing Neuromorphic Computing: Vertical Bulk Heterojunction Transistors with Multi-Neuromorphic Functions for Efficient Deep Learning . | ADVANCED MATERIALS , 2025 , 37 (27) . |
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Neuromorphic vision systems have the capacity to simulate the perception and processing of visual information by visual cells in the retina. However, when confronted with the challenges posed by a substantial volume of complex data and complex environments, traditional neuromorphic vision systems are unable to handle redundant signals of overenhancement and suppression. These systems are required to face the considerable challenges posed by complicated circuits and algorithms. In this paper, we present an adaptive synaptic transistor with a built-in heterojunction that can switch between three modes of synaptic excitation-inhibition effect, excitation-adaptive, and inhibition-adaptive photoconductivity effect by utilizing light switching and wavelength change. The device can complete the entire adaptation process from excitation sensitization to self-adaptation to the initial current in 1 s, and from excitation sensitization to adaptation in 3.2 s. The adaptation speed is superior to that of the human eye (5 min). The combination of convolutional neural networks (CNNs) with adaptive synaptic transistors has yielded the development of an advanced neuromorphic vision system. This system exhibits fast self-adaptation and static image recognition and classification capabilities, with a recognition rate that exceeds 90%, thereby facilitating the advancement of next-generation neuromorphic vision systems.
Keyword ：
adaptive adaptive habituation habituation heterojunction heterojunction neuromorphic phototransistors neuromorphic phototransistors quantum dots quantum dots sensitization sensitization
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| GB/T 7714 | Lin, Zexi , Zhao, Wenxiao , Lin, Xing et al. Neuromorphic Phototransistors with Built-in Heterojunction for Efficient and Accurate Adaptive Sensing [J]. | ACS PHOTONICS , 2025 , 12 (9) : 5121-5132 . |
| MLA | Lin, Zexi et al. "Neuromorphic Phototransistors with Built-in Heterojunction for Efficient and Accurate Adaptive Sensing" . | ACS PHOTONICS 12 . 9 (2025) : 5121-5132 . |
| APA | Lin, Zexi , Zhao, Wenxiao , Lin, Xing , Zhang, Liyan , Gao, Jiaqi , Xu, Sheng et al. Neuromorphic Phototransistors with Built-in Heterojunction for Efficient and Accurate Adaptive Sensing . | ACS PHOTONICS , 2025 , 12 (9) , 5121-5132 . |
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3D reconstruction is crucial in computer vision, especially in medical imaging and human-computer interaction. However, traditional reconstruction methods face challenges like energy inefficiency and memory limitations due to the storage-computation-separated architecture. Neuromorphic devices, inspired by the brain's architecture, offer a solution for efficient data processing. Electrical output synapse devices for 3D reconstruction face delays in coloring point clouds after depth processing, leading to errors. In this work, a co-planar quantum dot (QD) light-emitting synapse is proposed for high-precision 3D reconstruction. By using the light-emitting synapse, handwritten digit recognition achieved 92.35% accuracy in just 20 epochs. Depth and grayscale information are independently processed through electrical and optical outputs, allowing for parallel processing that enhances reconstruction quality. This method decreases losses by 46.3% and reduces the reconstruction pixel error rate by over 21% in comparison to the single output approach. This study demonstrates significant potential of light-emitting synapses in computer vision applications.
Keyword ：
3D reconstruction 3D reconstruction dual output parallel computing dual output parallel computing light-emitting synaptic device light-emitting synaptic device neuromorphic computing neuromorphic computing polar electrode bridged structure polar electrode bridged structure
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| GB/T 7714 | Lin, Tao , Yang, Chuiying , Chen, Cong et al. Bioinspired Quantum Dots Light-Emitting Synapse with Efficient Parallel Processing Capability for High-Precision 3D Reconstruction in Stereo Vision [J]. | SMALL , 2025 , 21 (36) . |
| MLA | Lin, Tao et al. "Bioinspired Quantum Dots Light-Emitting Synapse with Efficient Parallel Processing Capability for High-Precision 3D Reconstruction in Stereo Vision" . | SMALL 21 . 36 (2025) . |
| APA | Lin, Tao , Yang, Chuiying , Chen, Cong , Ye, Jiabin , Yang, Peng , Xu, Yuke et al. Bioinspired Quantum Dots Light-Emitting Synapse with Efficient Parallel Processing Capability for High-Precision 3D Reconstruction in Stereo Vision . | SMALL , 2025 , 21 (36) . |
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