Timing‐Dependent Spiking Neural Network: Board‐Level Hardware Implementation with Photoelectroactive Van der Waals Synapses
Abstract
Abstract The rapid growth of unstructured data in applications such as autonomous systems and edge AI underscores the urgent need for energy‐efficient, real‐time computing exemplified by biological brains, where synaptic weights are adjusted according to the timing of neural spikes, known as spike‐timing‐dependent plasticity (STDP). This work presents the first experimental realization of a multi‐channel timing‐dependent spiking neural network (TD‐SNN) at the board‐level by integrating photoelectroactive synaptic devices with an analog leaky integrate‐and‐fire (LIF) neuron circuit. The synaptic devices exploit the precise timing dependency between electrical presynaptic and optical postsynaptic spikes to emulate STDP, enabling reversible and bidirectional modulation of synaptic weights through photoelectroactive doping. By engineering the shape of presynaptic pulses, the devices demonstrate diverse biological STDP learning rules, including Hebbian, anti‐Hebbian, all‐LTP, and all‐LTD. Integrated single‐ and multi‐channel networks exhibit self‐learning, system‐level adaptive, and competitive behaviors. Experimentally extracted STDP parameters are implemented in SNN simulations, where network performance is determined by the long‐term potentiation/depression area ratio (LTP/D area ratio, PDR ) of the STDP curve. When PDR ≥ 1.25, robust pattern classification is achieved, reaching up to 90.9% accuracy on MNIST tasks. These results mark a milestone in timing‐dependent neuromorphic hardware, demonstrating device‐level feasibility toward adaptive and real‐time learning hardware.
Article Details
Authors (20)
Seongjun Kim
Division of Electrical Engineering Hanyang University ERICA Ansan 15588 Republic of Korea
Jeong‐Ick Cho
Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 Republic of Korea
Sungsoo Lee
Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 Republic of Korea
YoonChul Shin
Department of Materials Science and Chemical Engineering, Hanyang University 3 , Ansan 15588,
Je‐Jun Lee
Center of Quantum Technology, Post‐Silicon Semiconductor Institute Korea Institute of Science and Technology (KIST) Seoul 02792 Republic of Korea
Taehyuk Jang
Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 Republic of Korea
Hyeonjung Kim
Division of Electrical Engineering Hanyang University ERICA Ansan 15588 Republic of Korea
Junhwa Oh
Division of Electrical Engineering Hanyang University ERICA Ansan 15588 Republic of Korea
Sanghyun Lee
Kwanghee Ko
Division of Electrical Engineering Hanyang University ERICA Ansan 15588 Republic of Korea
Juncheol Kang
Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 Republic of Korea
Junseo Lee
Matthew T. Flavin
School of Electrical and Computer Engineering, Georgia Institute of Technology
Dong‐Ho Kang
School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju 61005 Republic of Korea
Byung Chul Jang
School of Electronic and Electrical Engineering Kyungpook National University Daegu Republic of Korea
Ji‐Hoon Ahn
Department of Materials Science and Chemical Engineering Hanyang University ERICA Ansan 15588 Republic of Korea
Yoonmyung Lee
Sang Min Won
Jin‐Hong Park
Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 Republic of Korea
Seyong Oh