Electrolyte‐Gated Vertical Synapse Array based on Van Der Waals Heterostructure for Parallel Computing

Abstract

Recently, three‐terminal synaptic devices, which separate read and write terminals, have attracted significant attention because they enable nondestructive read‐out and parallel‐access for updating synaptic weights. However, owing to their structural features, it is difficult to address the relatively high device density compared with two‐terminal synaptic devices. In this study, a vertical synaptic device featuring remotely controllable weight updates via e‐field‐dependent movement of mobile ions in the ion‐gel layer is developed. This synaptic device successfully demonstrates all essential synaptic characteristics, such as excitatory/inhibitory postsynaptic current (E/IPSC), paired‐pulse facilitation (PPF), and long‐term potentiation/depression (LTP/D) by electrical measurements, and exhibits competitive LTP/D characteristics with a dynamic range (Gmax/Gmin) of 31.3, and asymmetry (AS) of 8.56. The stability of the LTP/D characteristics is also verified through repeated measurements over 50 cycles; the relative standard deviations (RSDs) of Gmax/Gmin and AS are calculated as 1.65% and 0.25%, respectively. These excellent synaptic properties enable a recognition rate of ≈99% in the training and inference tasks for acoustic and emotional information patterns. This study is expected to be an important foundation for the realization of future parallel computing networks for energy‐efficient and high‐speed data processing.

Self-Powered Intelligent Tactile-Sensing System Based on Organic Electrochemical Transistors

“…22 The process of memory formation in the brain can be facilitated through repeated rehearsals or training sessions. The transition process from short-term memory (STM) to long-term memory (LTM) was simulated by applying pulsed V G with different durations (0.1 s, 1 s, 10 s, 20 s) and pulse numbers (5,20,50,100) to the OECT artificial synaptic device under a pressure of 0.2 kPa (Figure 3d and e). The change in the steady-state channel current before and after the application of V G is defined as the memory level.…”

2D multifunctional devices: from material preparation to device fabrication and neuromorphic applications

“…Hwang et al reported an ultra-flexible artificial synaptic array with concrete-mechanical cycling tolerance consisting of lithium silicate (LiSiO x ) with all-solid 2D MoS 2 channels (figure 5(c)) [31]. Recently, Oh et al developed an electrolyte-gated vertical synaptic array using a graphene/WS 2 heterojunction, as shown in figure 5(d) [121]. The electrolyte-gated vertical synaptic array not only highlights the advantages of lossless readout and parallel synaptic weight update of synaptic transistors but also shows the great potential of vertical transistors in crossbar arrays.…”

“…This work demonstrates the potential of building hardware neural networks for high-precision brain-like computing. Recently, Oh et al prepared an electrolyte-gated vertical synaptic array consisting of a graphene/WS 2 vdWH and an ionic gel [121]. By modulating the Fermi energy levels through ionic motion within the ion-gel weight-controlled layer, the LTP/D performance was successfully optimized.…”

“…Implementation of multifunctional 2D neuromorphic systems: so far, 2D optoelectronic synaptic devices have proven potential for applications in various artificial visual scenarios, such as CIR [141,143,145], adaptive image recognition [57,139,148], motion recognition [149], and 3D visual polarization imaging [103]. Benefiting from the tunable synaptic plasticity, 2D synaptic devices have also been employed to construct artificial auditory systems, such as sound localization [48,78] and acoustic pattern recognition [55,121]. Moreover, tactile sensing elements, grounded in diverse operational principles, have been incorporated into the construction of artificial tactile systems [53,155].…”

Memristor-based neural networks: a bridge from device to artificial intelligence

“…The deep hierarchical nonlinear processing of neural networks enables automatic and efficient learning of senior abstract representations in higher dimensional data, which has revolutionized the multimedia fields, such as images and videos, 41 speech and language. 42 It also plays an increasingly important role in the emerging fields of medical health and robotics. 43–45 With continuous performance improvement, neural networks are increasingly deployed in some practical industrial applications, including manufacturing, asset management, mobile phone voice assistance, autonomous driving, and so on.…”