Accelerated Learning in Wide-Band-Gap AlN Artificial Photonic Synaptic Devices: Impact on Suppressed Shallow Trap Level.

Journal: Nano letters
Published Date:

Abstract

Artificial synaptic platforms are promising for next-generation semiconductor computing devices; however, state-of-the-art optoelectronic approaches remain challenging, owing to their unstable charge trap states and limited integration. We demonstrate wide-band-gap (WBG) III-V materials for photoelectronic neural networks. Our experimental analysis shows that the enhanced crystallinity of WBG synapses promotes better synaptic characteristics, such as effective multilevel states, a wider dynamic range, and linearity, allowing the better power consumption, training, and recognition accuracy of artificial neural networks. Furthermore, light-frequency-dependent memory characteristics suggest that artificial optoelectronic synapses with improved crystallinity support the transition from short-term potentiation to long-term potentiation, implying a clear emulation of the psychological multistorage model. This is attributed to the charge trapping in deep-level states and suppresses fast decay and nonradiative recombination in shallow traps. We believe that the fingerprints of these WBG synaptic characteristics provide an effective strategy for establishing an artificial optoelectronic synaptic architecture for innovative neuromorphic computing.

Authors

  • Moonsang Lee
    Department of Materials Science and Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Republic of Korea.
  • Seunghyun Nam
    Department of Materials Science and Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Republic of Korea.
  • Byungjin Cho
    Department of Advanced Material Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 28644, Republic of Korea.
  • Ojun Kwon
    Department of Advanced Material Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 28644, Republic of Korea.
  • Hyun Uk Lee
    Division of Material Analysis and Research, Korea Basic Science Institute, Daejeon 305-333, Republic of Korea. Electronic address: leeho@kbsi.re.kr.
  • Myung Gwan Hahm
    Department of Materials Science and Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Republic of Korea.
  • Un Jeong Kim
    Imaging Device Laboratory, Samsung Advanced Institute of Technology, Suwon 443-803, Republic of Korea.
  • Hyungbin Son
    School of Integrative Engineering, Chung-Ang University, Seoul 06974, Republic of Korea.