Baek Ji Hyun, Kwak Kyung Ju, Kim Seung Ju, Kim Jaehyun, Kim Jae Young, Im In Hyuk, Lee Sunyoung, Kang Kisuk, Jang Ho Won
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea.
Advanced Institute of Convergence Technology, Seoul National University, Suwon, 16229, Korea.
Nanomicro Lett. 2023 Mar 21;15(1):69. doi: 10.1007/s40820-023-01035-3.
Recently, artificial synapses involving an electrochemical reaction of Li-ion have been attributed to have remarkable synaptic properties. Three-terminal synaptic transistors utilizing Li-ion intercalation exhibits reliable synaptic characteristics by exploiting the advantage of non-distributed weight updates owing to stable ion migrations. However, the three-terminal configurations with large and complex structures impede the crossbar array implementation required for hardware neuromorphic systems. Meanwhile, achieving adequate synaptic performances through effective Li-ion intercalation in vertical two-terminal synaptic devices for array integration remains challenging. Here, two-terminal Au/LiCoO/Pt artificial synapses are proposed with the potential for practical implementation of hardware neural networks. The Au/LiCoO/Pt devices demonstrated extraordinary neuromorphic behaviors based on a progressive dearth of Li in LiCoO films. The intercalation and deintercalation of Li-ion inside the films are precisely controlled over the weight control spike, resulting in improved weight control functionality. Various types of synaptic plasticity were imitated and assessed in terms of key factors such as nonlinearity, symmetricity, and dynamic range. Notably, the LiCoO-based neuromorphic system outperformed three-terminal synaptic transistors in simulations of convolutional neural networks and multilayer perceptrons due to the high linearity and low programming error. These impressive performances suggest the vertical two-terminal Au/LiCoO/Pt artificial synapses as promising candidates for hardware neural networks.
最近，涉及锂离子电化学反应的人工突触被认为具有显著的突触特性。利用锂离子嵌入的三端突触晶体管通过利用稳定离子迁移导致的非分布式权重更新优势，展现出可靠的突触特性。然而，具有大型复杂结构的三端配置阻碍了硬件神经形态系统所需的交叉阵列实现。同时，通过在用于阵列集成的垂直两终端突触器件中进行有效的锂离子嵌入来实现足够的突触性能仍然具有挑战性。在此，提出了具有硬件神经网络实际应用潜力的两终端金/锂钴氧化物/铂人工突触。金/锂钴氧化物/铂器件基于锂钴氧化物薄膜中锂的逐渐缺乏展现出非凡的神经形态行为。通过权重控制尖峰精确控制薄膜内锂离子的嵌入和脱嵌，从而改善了权重控制功能。根据非线性、对称性和动态范围等关键因素对各种类型的突触可塑性进行了模拟和评估。值得注意的是，基于锂钴氧化物的神经形态系统在卷积神经网络和多层感知器的模拟中由于高线性度和低编程误差而优于三端突触晶体管。这些令人印象深刻的性能表明垂直两终端金/锂钴氧化物/铂人工突触是硬件神经网络的有前途的候选者。
