Electrolyte‐Gated Vertical Synapse Array based on Van Der Waals Heterostructure for Parallel Computing. Issue 6 (26th December 2021) - Oh, Seyong; Lee, Ju‐Hee; Seo, Seunghwan; Choo, Hyongsuk; Lee, Dongyoung; Cho, Jeong‐Ick; Park, Jin‐Hong
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 ( G max / G min ) 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 G max / G min 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. Abstract : New type of vertical synapse array with long‐term plasticity is implemented by using van der Waals heterostructure and ion‐gel weight control layer. Important synaptic properties including long‐term potentiation/depression are analyzed through electrical measurements, and the applicability of the vertical synaptic device to artificial neural networks is demonstrated via training and inference tasks for acoustic and emotional information patterns.