Two-Terminal Lithium-Mediated Artificial Synapses with Enhanced Weight Modulation for Feasible Hardware Neural Networks

e-ISSN 2150-5551 CN 31-2103/TB ARTICLE Cite as Nano-Micro Lett. (2023) 15:69 Received: 25 November 2022 Accepted: 1 February 2023 © The Author(s) 2023 https://doi.org/10.1007/s40820-023-01035-3 Two‑Terminal Lithium‑Mediated Artificial Synapses with Enhanced Weight Modulation for Feasible Hardware Neural Networks Ji Hyun Baek1 , Kyung Ju Kwak1, Seung Ju Kim1 , Jaehyun Kim1 , Jae Young Kim1 , In Hyuk Im1 , Sunyoung Lee1, Kisuk Kang1 *, Ho Won Jang1,2 * HIGHLIGHTS • The Li-mediated artificial synapses with a vertical two-terminal configuration capable of various synaptic behaviors, including bioplausible synaptic plasticity, were successfully demonstrated for the first time and thoroughly explored • Synaptic characteristics based on the progressive dearth of Li in L ixCo2 films are precisely controlled over the weight control spike, achieving extraordinary weight control functionality. • In artificial neural network simulation, LixCoO2-based neuromorphic system showed excellent accuracy comparable to the theoretical maximum due to the low nonlinearity and programming error, suggesting feasibility of hardware neural network implementation. ABSTRACT 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 nondistributed 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/LixCoO2/Pt artificial synapses are proposed with the potential for practical implementation of hardware neural networks. The Au/Li xCoO2/Pt devices demonstrated extraordinary neuromorphic behaviors based on a progressive dearth of Li in L ixCoO2 films. The intercalation and deinter- calation 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 * Kisuk Kang, ; Ho Won Jang, 1 Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea 2 Advanced Institute of Convergence Technology, Seoul National University, Suwon 16229, Korea Vol.:(0123456789) 13 69 Page 2 of 18 Nano-Micro Lett. (2023) 15:69 range. Notably, the L ixCoO2-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/LixCoO2/Pt artificial synapses as promising candidates for hardware neural networks KEYWORDS Artificial synapse; Neuromorphic; Li-based; Two-terminal; Synaptic plasticity 1 Introduction With the advent of the machine learning era, human-generated unstructured data such as text, images, and audio are exploding. Processing vast amounts of data with a conventional computing system based on the von Neumann architecture has reached its limits [1, 2]. The challenges for modern computing systems originate from the reduced efficiency of energy and throughput caused by constant data transfer between memory and processing units, well known as the von Neumann bottleneck [3, 4]. Neuromorphic computing inspired by the functionality of the human brain has received considerable attention as one of the ways to achieve technical requirements to overcome von Neumann bottlenecks [5–8]. Computing technology that executes massively parallel processing in an energy-efficient manner can handle such unstructured data productively. The brain-inspired computing system can be realized as a hardware implementation of a neural network platform made up of combinations of numerous artificial neurons and synapses [9, 10]. In neuroscience, a synapse is a functional junction between two neurons that transmits signals from the pre-synaptic neuron to the post-synaptic neuron. Synaptic weight, also known as synaptic connection and synaptic efficacy, stands for the amount of influence one neuron has on another. The majority of the development, memory, and learning in neural networks are attributed to synaptic plasticity, which refers to activity-dependent modifications of synaptic weights [11–13]. In a neuromorphic system, synaptic weight can correspond to the amplitude or strength of a connection between two nodes, in other words, the conductance of artificial synaptic elements [14]. Artificial synaptic devices associated with various materials and working mechanisms have been extensively studied in recent years [15–18]. In particular, vertical two-terminal memristive devices including electrochemical metallization memory (ECM) [19, 20], valence change memory (VCM) [21, 22], and phase-change memory (PCM) [23, 24] are regarded as probable candidates for artificial synapses © The authors owing to their simplicity of fabrication and extensibility of structural integration as crossbar arrays [25–27]. Nevertheless, these conventional memristive synaptic devices have intrinsic difficulties in precise weight control due to their random nature, resulting in nonlinear and asymmetric weight updates that significantly degrade the performance of artificial neural networks. Hence, their practical application as synaptic elements in hardware neuromorphic systems is severely restricted. Whereas, three-terminal synaptic transistors have attracted substantial interest due to reliable and notable synaptic characteristics [28–31]. Employing completely independent terminals for programming (gate) and reading (drain) facilitates linear and less distributed weight control operation. Recently, three-terminal synaptic devices associated with the electrochemical reactions of Li ions have been discovered to have improved synaptic properties [32–36]. Li ions diffuse gradually from the matrix in response to external stimuli, ensuring high controllability in plasticity modification. In addition, the migrations of Li+ ions do not induce considerable structural deformation upon intercalation and deintercalation, allowing stable and reversible operation. Synaptic transistors, despite their remarkable ability to perform linear and noise-free weight updates, have fundamental limitations in the realization of hardware neuromorphic systems. The three-terminal configurations with large and complex structures impede the crossbar array implementation required for high-density integration. There are some stu (...truncated)