Fully Light‐Controlled Memory and Neuromorphic Computation in Layered Black Phosphorus. Issue 10 (17th November 2020)

• Record Type: Journal Article
• Authors: Ahmed, Taimur; Tahir, Muhammad; Low, Mei Xian; Ren, Yanyun; Tawfik, Sherif Abdulkader; Mayes, Edwin L. H.; Kuriakose, Sruthi; Nawaz, Shahid; Spencer, Michelle J. S.; Chen, Hua; Bhaskaran, Madhu; Sriram, Sharath; Walia, Sumeet
• Is Part Of: Advanced materials. Volume 33:Issue 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-17
• Subjects: artificial neural networks -- black phosphorus -- machine learning -- neuromorphics -- optical memory; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.202004207
• Languages: English
• ISSNs: 0935-9648

Abstract

Abstract: Imprinting vision as memory is a core attribute of human cognitive learning. Fundamental to artificial intelligence systems are bioinspired neuromorphic vision components for the visible and invisible segments of the electromagnetic spectrum. Realization of a single imaging unit with a combination of in‐built memory and signal processing capability is imperative to deploy efficient brain‐like vision systems. However, the lack of a platform that can be fully controlled by light without the need to apply alternating polarity electric signals has hampered this technological advance. Here, a neuromorphic imaging element based on a fully light‐modulated 2D semiconductor in a simple reconfigurable phototransistor structure is presented. This standalone device exhibits inherent characteristics that enable neuromorphic image pre‐processing and recognition. Fundamentally, the unique photoresponse induced by oxidation‐related defects in 2D black phosphorus (BP) is exploited to achieve visual memory, wavelength‐selective multibit programming, and erasing functions, which allow in‐pixel image pre‐processing. Furthermore, all‐optically driven neuromorphic computation is demonstrated by machine learning to classify numbers and recognize images with an accuracy of over 90%. The devices provide a promising approach toward neurorobotics, human–machine interaction technologies, and scalable bionic systems with visual data storage/buffering and processing. Abstract : An all‐optically tunable neuromorphic imaging element based on black phosphorus (BP) is demonstrated. The unusual wavelength‐dependent photocurrent in BP is harnessed to optically program and erase visual memory elements. Concurrently, the same elements are capable of in‐pixel image pre‐processing in an array and optoelectronic machine learning for image recognition through artificial neural networks.