Jang, Jingon Gi, Sanggyun Yeo, Injune Choi, Sanghyeon Jang, Seonghoon Ham, Seonggil Lee, Byunggeun Wang, Gunuk 2024-01-19T11:32:33Z 2024-01-19T11:32:33Z 2022-06-17 2022-08 2198-3844 https://pubs.kist.re.kr/handle/201004/114840 Realization of memristor-based neuromorphic hardware system is important to achieve energy efficient bigdata processing and artificial intelligence in integrated device system-level. In this sense, uniform and reliable titanium oxide (TiOx) memristor array devices are fabricated to be utilized as constituent device element in hardware neural network, representing passive matrix array structure enabling vector-matrix multiplication process between multisignal and trained synaptic weight. In particular, in situ convolutional neural network hardware system is designed and implemented using a multiple 25 x 25 TiOx memristor arrays and the memristor device parameters are developed to bring global constant voltage programming scheme for entire cells in crossbar array without any voltage tuning peripheral circuit such as transistor. Moreover, the learning rate modulation during in situ hardware training process is successfully achieved due to superior TiOx memristor performance such as threshold uniformity (approximate to 2.7%), device yield (> 99%), repetitive stability (approximate to 3000 spikes), low asymmetry value of approximate to 1.43, ambient stability (6 months), and nonlinear pulse response. The learning rate modulable fast-converging in situ training based on direct memristor operation shows five times less training iterations and reduces training energy compared to the conventional hardware in situ training at approximate to 95.2% of classification accuracy. English Wiley-VCH Verlag A Learning-Rate Modulable and Reliable TiOx Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing Article 10.1002/advs.202201117 1 Advanced Science, v.9, no.22 Advanced Science 9 22 Y scie scopus 000806004500001 Chemistry, Multidisciplinary Nanoscience & Nanotechnology Materials Science, Multidisciplinary Chemistry Science & Technology - Other Topics Materials Science Article RESISTIVE SWITCHING MEMORY DEVICE SYNAPSE SYSTEM artificial synapses hardware implementation memristors neuromorphic computing uniformity