JOURNAL ARTICLE

Multifunctional 2D MoS2 Optoelectronic Artificial Synapse with Integrated Arithmetic and Reconfigurable Logic Operations for In‐Memory Neuromorphic Computing Applications.

  • Published In: Advanced Materials Technologies, 2023, v. 8, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Sahu, Mousam Charan; Sahoo, Sandhyarani; Mallik, Sameer Kumar; Jena, Anjan Kumar; Sahoo, Satyaprakash 3 of 3

Abstract

The hardware implementation of advanced artificial intelligence (AI) technology based on complex deep learning and machine learning algorithms is constricted by the limitation of conventional Von–Neuman architecture. Emerging neuromorphic computing architecture based on the human brain with in‐memory computing capability could instigate unprecedented breakthroughs in AI technology. In this pursuit, 2D MoS2 optoelectronic artificial synapse imitating complex biological neuromorphic behavior such as short/long‐term memory, paired‐pulse facilitation, and long‐term depression‐potentiation is proposed and demonstrated. Furthermore, the broadband sensitivity of the device can be utilized to emulate Pavlov's classical conditioning for associative learning of the biological brain. More importantly, reconfigurable Boolean AND and OR logic gate operation is demonstrated within the same device by synergistically modulating the device conductance via the persistent photoconductivity and electrical gate stress. The linear response of the photocurrent to the optical stimulus can perform arithmetic operations such as counting, addition, and subtraction within a single device. This novel integration of memory, synaptic behavior, and processing within a single monolayer MoS2 device is believed to put forth a new horizon for the Non‐Von–Neuman type in‐memory computing architecture for highly advanced AI applications based on 2D materials. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Advanced Materials Technologies. 2023/01, Vol. 8, Issue 2, p1
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2023
  • ISSN:2365-709X
  • DOI:10.1002/admt.202201125
  • Accession Number:161474057
  • Copyright Statement:Copyright of Advanced Materials Technologies is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.