Memristors have become very popular to perform advanced computation and construct artificial neural networks (ANNs). However, memristors made of traditional materials still cannot exhibit the performance desired for these applications. The integration of two-dimensional (2D) materials into solid-state electronic devices can extend the performance of memristors, enabling their use to construct ANNs. Such devices can exhibit properties that metal-oxide memristors do not have, including high thermal stability, coexistence of threshold and bipolar resistive switching (RS), high controllability of potentiation, depression and relaxation and excellent mechanical stability and transparency.
Recently, the research group of Prof. Mario Lanza published a cover article titled ‘Wafer-scale integration of two-dimensional materials in high-density memristive crossbar arrays for artificial neural networks’ in Nature Electronics. They reported the fabrication and statistical analysis of high-density memristive crossbar arrays made of 2D layered materials, and use them to model an artificial neural network for image recognition. The arrays exhibit a high yield (98%), low cycle-to-cycle variability (1.53%) and low device-to-device variability (5.74%). The devices exhibit different switching mechanisms depending on the electrode material used as well as characteristics that make them suitable for application in neuromorphic circuits. The ANNs emulated with these devices show high accuracy of 98%, and the inter-cell disturbance is very low.
Link to Paper: https://www.nature.com/articles/s41928-020-00473-w
Link to Prof. Lanza’s Group: http://funsom.suda.edu.cn/funsomen/c4/04/c3002a50180/page.htm
Editor: Danting Xiang