All-Solid-State Synaptic Transistors with Lithium-Ion-Based Electrolytes for Linear Weight Mapping and Update in Neuromorphic Computing Systems

Ji-Min Park; Hwiho Hwang; Min Suk Song; Seong Cheol Jang; Jung Hyun Kim; Hyungjin Kim; Hyun-Suk Kim

doi:10.1021/acsami.3c09162

All-Solid-State Synaptic Transistors with Lithium-Ion-Based Electrolytes for Linear Weight Mapping and Update in Neuromorphic Computing Systems

ACS Appl Mater Interfaces. 2023 Oct 11;15(40):47229-47237. doi: 10.1021/acsami.3c09162. Epub 2023 Oct 2.

Authors

Ji-Min Park^{1

2}, Hwiho Hwang³, Min Suk Song³, Seong Cheol Jang^{1

2}, Jung Hyun Kim⁴, Hyungjin Kim³, Hyun-Suk Kim^{1

2}

Affiliations

¹ Department of Materials Science and Engineering, Chungnam National University, Daejeon 34134, Republic of Korea.
² Department of Energy and Materials Engineering, Dongguk University, Seoul 04620, Republic of Korea.
³ Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea.
⁴ Department of Advanced Materials Science and Engineering, Hanbat National University, 125, Dongseo-daero, Yuseong-gu, Daejeon 34158, Republic of Korea.

PMID: 37782228
DOI: 10.1021/acsami.3c09162

Abstract

Neuromorphic computing, an innovative technology inspired by the human brain, has attracted increasing attention as a promising technology for the development of artificial intelligence systems. This study proposes synaptic transistors with a Li_1-xAl_xTi_2-x(PO₄)₃ (LATP) layer to analyze the conductance modulation linearity, which is essential for weight mapping and updating during on-chip learning processes. The high ionic conductivity of the LATP electrolyte provides a large hysteresis window and enables linear weight update in synaptic devices. The results demonstrate that optimizing the LATP layer thickness improves the conductance modulation and linearity of synaptic transistors during potentiation and degradation. A 20 nm-thick LATP layer results in the most nonlinear depression (α_d = -6.59), whereas a 100 nm-thick LATP layer results in the smallest nonlinearity (α_d = -2.22). Additionally, a device with the optimal 100 nm-thick LATP layer exhibits the highest average recognition accuracy of 94.8% and the smallest fluctuation, indicating that the linearity characteristics of a device play a crucial role in weight update during learning and can significantly affect the recognition accuracy.

Keywords: Li1−xAlxTi2−x(PO4)3; high ionic conductivity; neuromorphic computing; solid-state electrolyte; synaptic device.