Device-Algorithm Co-Optimization for an On-Chip Trainable Capacitor-Based Synaptic Device with IGZO TFT and Retention-Centric Tiki-Taka Algorithmopen access
- Authors
- Won, Jongun; Kang, Jaehyeon; Hong, Sangjun; Han, Narae; Kang, Minseung; Park, Yeaji; Roh, Youngchae; Seo, Hyeong Jun; Joe, Changhoon; Cho, Ung; Kang, Minil; Um, Minseong; Lee, Kwang-Hee; Yang, Jee-Eun; Jung, Moonil; Lee, Hyung-Min; Oh, Saeroonter; Kim, Sangwook; Kim, Sangbum
- Issue Date
- Oct-2023
- Publisher
- Wiley-VCH Verlag
- Keywords
- device-algorithm co-optimization; in-memory computing; indium gallium zinc oxide thin film transistor (IGZO TFT); neuromorphic; tiki-taka algorithm
- Citation
- Advanced Science, v.10, no.29, pp 1 - 11
- Pages
- 11
- Indexed
- SCIE
SCOPUS
- Journal Title
- Advanced Science
- Volume
- 10
- Number
- 29
- Start Page
- 1
- End Page
- 11
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114391
- DOI
- 10.1002/advs.202303018
- ISSN
- 2198-3844
2198-3844
- Abstract
- Analog in-memory computing synaptic devices are widely studied for efficient implementation of deep learning. However, synaptic devices based on resistive memory have difficulties implementing on-chip training due to the lack of means to control the amount of resistance change and large device variations. To overcome these shortcomings, silicon complementary metal-oxide semiconductor (Si-CMOS) and capacitor-based charge storage synapses are proposed, but it is difficult to obtain sufficient retention time due to Si-CMOS leakage currents, resulting in a deterioration of training accuracy. Here, a novel 6T1C synaptic device using only n-type indium gaIlium zinc oxide thin film transistor (IGZO TFT) with low leakage current and a capacitor is proposed, allowing not only linear and symmetric weight update but also sufficient retention time and parallel on-chip training operations. In addition, an efficient and realistic training algorithm to compensate for any remaining device non-idealities such as drifting references and long-term retention loss is proposed, demonstrating the importance of device-algorithm co-optimization. © 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.
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