Kim, Min Jee Im, Jaegwang Kim, Keonhee Jo, Yooyeon Noh, Gichang Park, Eunpyo Lee, Dae Kyu Kim, Inho Jeong, YeonJoo Lee, Hyung-Min Kwak, Joon Young 2025-04-23T05:30:13Z 2025-04-23T05:30:13Z 2025-03-20 2024-10 2163-4025 https://pubs.kist.re.kr/handle/201004/152285 This paper proposes an SNN-based system with 2T1R architecture capable of interfacing with emerging neuromorphic devices that emulate crucial components such as neurons and synapses in neural network. This system proposes a spike regenerator that enables the effective use of neuron device spike signals and a Spike-Timing-Dependent Plasticity (STDP) generator to update synaptic device utilizing update pulse train for the STDP. This compact system allows simple adjustments of regenerated waveforms and STDP pulse widths to meet various device specifications. The measurement results using hBN-based pre-synaptic neuron device and Cu:Te- based CBRAM synaptic device demonstrate the feasibility of learning through the system. This system supports on-chip learning network operations with various devices as well as hBN and Cu:Te devices. In the future research, this system can be expanded for large-scale array implementations. English IEEE Adaptive Spiking Neural Network Neuromorphic Hardware for Interfacing Between Emerging Neuron and Synaptic Devices Conference 10.1109/BioCAS61083.2024.10798361 1 2024 Biomedical Circuits and Systems Conference 2024 Biomedical Circuits and Systems Conference US Xi'an, PEOPLES R CHINA 2024-10-24 2024 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE, BIOCAS 2024 001411993700147 2-s2.0-85216255623