Mixed-Dimensional Formamidinium Bismuth Iodides Featuring In-Situ Formed Type-I Band Structure for Convolution Neural Networks

  • June Mo Yang
  • , Ju Hee Lee
  • , Young Kwang Jung
  • , So Yeon Kim
  • , Jeong Hoon Kim
  • , Seul Gi Kim
  • , Jeong Hyeon Kim
  • , Seunghwan Seo
  • , Dong Am Park
  • , Jin Wook Lee
  • , Aron Walsh
  • , Jin Hong Park
  • , Nam Gyu Park

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

For valence change memory (VCM)-type synapses, a large number of vacancies help to achieve very linearly changed dynamic range, and also, the low activation energy of vacancies enables low-voltage operation. However, a large number of vacancies increases the current of artificial synapses by acting like dopants, which aggravates low-energy operation and device scalability. Here, mixed-dimensional formamidinium bismuth iodides featuring in-situ formed type-I band structure are reported for the VCM-type synapse. As compared to the pure 2D and 0D phases, the mixed phase increases defect density, which induces a better dynamic range and higher linearity. In addition, the mixed phase decreases conductivity for non-paths despite a large number of defects providing lots of conducting paths. Thus, the mixed phase-based memristor devices exhibit excellent potentiation/depression characteristics with asymmetricity of 3.15, 500 conductance states, a dynamic range of 15, pico ampere-scale current level, and energy consumption per spike of 61.08 aJ. A convolutional neural network (CNN) simulation with the Canadian Institute for Advanced Research-10 (CIFAR-10) dataset is also performed, confirming a maximum recognition rate of approximately 87%. This study is expected to lay the groundwork for future research on organic bismuth halide-based memristor synapses usable for a neuromorphic computing system.

Original languageEnglish
Article number2200168
JournalAdvanced Science
Volume9
Issue number14
DOIs
StatePublished - 16 May 2022

Bibliographical note

Publisher Copyright:
© 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.

Keywords

  • artificial synapses
  • convolution neural networks
  • energy consumption
  • formamidinium bismuth iodide
  • memristors
  • mixed-dimensional
  • type I band alignment

Fingerprint

Dive into the research topics of 'Mixed-Dimensional Formamidinium Bismuth Iodides Featuring In-Situ Formed Type-I Band Structure for Convolution Neural Networks'. Together they form a unique fingerprint.

Cite this