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

9 Scopus citations


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
Issue number14
StatePublished - 16 May 2022

Bibliographical note

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


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


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