Park, Eunpyo Jang, Suyeon Noh, Gichang Jo, Yooyeon Lee, Dae Kyu Kim, In Soo Song, Hyun-Cheol Kim, Sangbum Kwak, Joon Young 2024-01-19T08:31:28Z 2024-01-19T08:31:28Z 2023-11-08 2023-10 1530-6984 https://pubs.kist.re.kr/handle/201004/113178 Recently, neuromorphic computing has been proposed to overcome the drawbacks of the current von Neumann computing architecture. Especially, spiking neural network (SNN) has received significant attention due to its ability to mimic the spike-driven behavior of biological neurons and synapses, potentially leading to low-power consumption and other advantages. In this work, we designed the indium-gallium-zinc oxide (IGZO) channel charge-trap flash (CTF) synaptic device based on a HfO2/Al2O3/Si3N4/Al2O3 layer. Our IGZO-based CTF device exhibits synaptic functions with 128 levels of synaptic weight states and spike-timing-dependent plasticity. The SNN-restricted Boltzmann machine was used to simulate the fabricated CTF device to evaluate the efficiency for the SNN system, achieving the high pattern-recognition accuracy of 83.9%. We believe that our results show the suitability of the fabricated IGZO CTF device as a synaptic device for neuromorphic computing. English American Chemical Society Indium-Gallium-Zinc Oxide-Based Synaptic Charge Trap Flash for Spiking Neural Network-Restricted Boltzmann Machine Article 10.1021/acs.nanolett.3c03510 1 Nano Letters, v.23, no.20, pp.9626 - 9633 Nano Letters 23 20 9626 9633 N scie scopus 001090715800001 2-s2.0-85175271496 Chemistry, Multidisciplinary Chemistry, Physical Nanoscience & Nanotechnology Materials Science, Multidisciplinary Physics, Applied Physics, Condensed Matter Chemistry Science & Technology - Other Topics Materials Science Physics Article MEMORY GATE TRANSISTOR DEVICE nonvolatile memory charge trap flash neuromorphiccomputing oxide semiconductor spiking neural network