Park Ji-Min, Hwang Hwiho, Song Min Suk, Jang Seong Cheol, Kim Jung Hyun, Kim Hyungjin, Kim Hyun-Suk
Department of Materials Science and Engineering, Chungnam National University, Daejeon 34134, Republic of Korea.
Department of Energy and Materials Engineering, Dongguk University, Seoul 04620, Republic of Korea.
ACS Appl Mater Interfaces. 2023 Oct 11;15(40):47229-47237. doi: 10.1021/acsami.3c09162. Epub 2023 Oct 2.
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 LiAlTi(PO) (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 = -6.59), whereas a 100 nm-thick LATP layer results in the smallest nonlinearity (α = -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.
神经形态计算作为一种受人类大脑启发的创新技术，作为人工智能系统发展的一项有前途的技术，已引起越来越多的关注。本研究提出了一种具有LiAlTi(PO)（LATP）层的突触晶体管，以分析电导调制线性度，这对于片上学习过程中的权重映射和更新至关重要。LATP电解质的高离子电导率提供了一个大的滞后窗口，并能在突触器件中实现线性权重更新。结果表明，优化LATP层厚度可改善突触晶体管在增强和退化过程中的电导调制和线性度。20nm厚的LATP层导致最非线性的抑制（α = -6.59），而100nm厚的LATP层导致最小的非线性度（α = -2.22）。此外，具有最佳100nm厚LATP层的器件表现出最高的平均识别准确率94.8%和最小的波动，这表明器件的线性度特性在学习过程中的权重更新中起着关键作用，并且会显著影响识别准确率。
