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Long Duration Persistent Photocurrent in 3 nm Thin Doped Indium Oxide for Integrated Light Sensing and In‐Sensor Neuromorphic Computation.

Authors :
Mazumder, Aishani
Nguyen, Chung Kim
Aung, Thiha
Low, Mei Xian
Rahman, Md. Ataur
Russo, Salvy P.
Tawfik, Sherif Abdulkader
Wang, Shifan
Bullock, James
Krishnamurthi, Vaishnavi
Syed, Nitu
Ranjan, Abhishek
Zavabeti, Ali
Abidi, Irfan H.
Guo, Xiangyang
Li, Yongxiang
Ahmed, Taimur
Daeneke, Torben
Al‐Hourani, Akram
Balendhran, Sivacarendran
Source :
Advanced Functional Materials; 9/5/2023, Vol. 33 Issue 36, p1-8, 8p
Publication Year :
2023

Abstract

Miniaturization and energy consumption by computational systems remain major challenges to address. Optoelectronics based synaptic and light sensing provide an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single‐element image sensors that allow reception of information and execution of in‐memory computing processes while maintaining memory for much longer durations without the need for frequent electrical or optical rehearsals. In this work, ultra‐thin (<3 nm) doped indium oxide (In2O3) layers are engineered to demonstrate a monolithic two‐terminal ultraviolet (UV) sensing and processing system with long optical state retention operating at 50 mV. This endows features of several conductance states within the persistent photocurrent window that are harnessed to show learning capabilities and significantly reduce the number of rehearsals. The atomically thin sheets are implemented as a focal plane array (FPA) for UV spectrum based proof‐of‐concept vision system capable of pattern recognition and memorization required for imaging and detection applications. This integrated light sensing and memory system is deployed to illustrate capabilities for real‐time, in‐sensor memorization, and recognition tasks. This study provides an important template to engineer miniaturized and low operating voltage neuromorphic platforms across the light spectrum based on application demand. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1616301X
Volume :
33
Issue :
36
Database :
Complementary Index
Journal :
Advanced Functional Materials
Publication Type :
Academic Journal
Accession number :
171385911
Full Text :
https://doi.org/10.1002/adfm.202303641