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2D Reconfigurable Memory Device Enabled by Defect Engineering for Multifunctional Neuromorphic Computing.
Xia, Yunpeng; Lin, Ning; Zha, Jiajia; Huang, Haoxin; Zhang, Yiwen; Liu, Handa; Tong, Jinyi; Xu, Songcen; Yang, Peng; Wang, Huide; Zheng, Long; Zhang, Zhuomin; Yang, Zhengbao; Chen, Ye; Chan, Hau Ping; Wang, Zhongrui; Tan, Chaoliang.
Affiliation
  • Xia Y; Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China.
  • Lin N; Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong SAR, 999077, China.
  • Zha J; Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong SAR, 999077, China.
  • Huang H; Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China.
  • Zhang Y; Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China.
  • Liu H; Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China.
  • Tong J; Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China.
  • Xu S; Department of Electrical and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, 999077, China.
  • Yang P; College of Integrated Circuits and Optoelectronic Chips, Shenzhen Technology University, Shenzhen, 518118, China.
  • Wang H; College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China.
  • Zheng L; Department of Chemistry, Chinese University of Hong Kong, Hong Kong SAR, 999077, China.
  • Zhang Z; Department of Mechanical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China.
  • Yang Z; Department of Mechanical and Aerospace Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, 999077, China.
  • Chen Y; Department of Mechanical and Aerospace Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, 999077, China.
  • Chan HP; Department of Chemistry, Chinese University of Hong Kong, Hong Kong SAR, 999077, China.
  • Wang Z; Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China.
  • Tan C; Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong SAR, 999077, China.
Adv Mater ; : e2403785, 2024 Jul 15.
Article in En | MEDLINE | ID: mdl-39007279
ABSTRACT
In this era of artificial intelligence and Internet of Things, emerging new computing paradigms such as in-sensor and in-memory computing call for both structurally simple and multifunctional memory devices. Although emerging two-dimensional (2D) memory devices provide promising solutions, the most reported devices either suffer from single functionalities or structural complexity. Here, this work reports a reconfigurable memory device (RMD) based on MoS2/CuInP2S6 heterostructure, which integrates the defect engineering-enabled interlayer defects and the ferroelectric polarization in CuInP2S6, to realize a simplified structure device for all-in-one sensing, memory and computing. The plasma treatment-induced defect engineering of the CuInP2S6 nanosheet effectively increases the interlayer defect density, which significantly enhances the charge-trapping ability in synergy with ferroelectric properties. The reported device not only can serve as a non-volatile electronic memory device, but also can be reconfigured into optoelectronic memory mode or synaptic mode after controlling the ferroelectric polarization states in CuInP2S6. When operated in optoelectronic memory mode, the all-in-one RMD could diagnose ophthalmic disease by segmenting vasculature within biological retinas. On the other hand, operating as an optoelectronic synapse, this work showcases in-sensor reservoir computing for gesture recognition with high energy efficiency.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Adv Mater Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Adv Mater Year: 2024 Document type: Article