2023-06-01 ロスアラモス国立研究所(LANL)
◆メモリスティブデバイスは、プログラミングとメモリの機能を持つ回路技術であり、従来の抵抗器技術とは異なり、電源を切っても電気状態を記憶することができます。これは人間の脳のような能力を持ち、コンピューティングとデバイスに新たな可能性を開拓します。ニューロモーフィックコンピューティングは、人間の脳のデータ保存と処理のアーキテクチャと能力を模倣することで、従来のコンピューティングの制約を克服し、エネルギー効率の高い高性能なコンピューティングを実現する手段となります。
<関連情報>
- https://discover.lanl.gov/news/0601-artificial-synapses/
- https://onlinelibrary.wiley.com/doi/10.1002/aisy.202300035
人工シナプスとニューロモルフィック・コンピューティングのためのインターフェース型メムリスティブ・デバイス An Interface-Type Memristive Device for Artificial Synapse and Neuromorphic Computing
Sundar Kunwar, Zachary Jernigan, Zach Hughes, Chase Somodi, Michael D. Saccone, Francesco Caravelli, Pinku Roy, Di Zhang, Haiyan Wang, Quanxi Jia, Judith L. MacManus-Driscoll, Garrett Kenyon, Andrew Sornborger, Wanyi Nie, Aiping Chen
Advanced Intelligent Systems Published: 26 April 2023
DOI:https://doi.org/10.1002/aisy.202300035
Abstract
Interface-type (IT) metal/oxide Schottky memristive devices have attracted considerable attention over filament-type (FT) devices for neuromorphic computing because of their uniform, filament-free, and analog resistive switching (RS) characteristics. The most recent IT devices are based on oxygen ions and vacancies movement to alter interfacial Schottky barrier parameters and thereby control RS properties. However, the reliability and stability of these devices have been significantly affected by the undesired diffusion of ionic species. Herein, a reliable interface-dominated memristive device is demonstrated using a simple Au/Nb-doped SrTiO3 (Nb:STO) Schottky structure. The Au/Nb:STO Schottky barrier modulation by charge trapping and detrapping is responsible for the analog resistive switching characteristics. Because of its interface-controlled RS, the proposed device shows low device-to-device, cell-to-cell, and cycle-to-cycle variability while maintaining high repeatability and stability during endurance and retention tests. Furthermore, the Au/Nb:STO IT memristive device exhibits versatile synaptic functions with an excellent uniformity, programmability, and reliability. A simulated artificial neural network with Au/Nb:STO synapses achieves a high recognition accuracy of 94.72% for large digit recognition from MNIST database. These results suggest that IT resistive switching can be potentially used for artificial synapses to build next-generation neuromorphic computing.
