Flexible Neural Network Realized by the Probabilistic SiOx Memristive Synaptic Array for Energy-Efficient Image Learning

Sanghyeon Choi, Jingon Jang, Min Seob Kim, Nam Dong Kim, Jeehyun Kwag, Gunuk Wang

Research output: Contribution to journalArticlepeer-review


The human brain's neural networks are sparsely connected via tunable and probabilistic synapses, which may be essential for performing energy-efficient cognitive and intellectual functions. In this sense, the implementation of a flexible neural network with probabilistic synapses is a first step toward realizing the ultimate energy-efficient computing framework. Here, inspired by the efficient threshold-tunable and probabilistic rod-to-rod bipolar synapses in the human visual system, a 16 × 16 crossbar array comprising the vertical form of gate-tunable probabilistic SiOx memristive synaptic barristor utilizing the Si/graphene heterojunction is designed and fabricated. Controllable stochastic switching dynamics in this array are achieved via various input voltage pulse schemes. In particular, the threshold tunability via electrostatic gating enables the efficient in situ alteration of the probabilistic switching activation (PAct) from 0 to 1.0, and can even modulate the degree of the PAct change. A drop-connected algorithm based on the PAct is constructed and used to successfully classify the shapes of several fashion items. The suggested approach can decrease the learning energy by up to ≈2,116 times relative to that of the conventional all-to-all connected network while exhibiting a high recognition accuracy of ≈93 %.

Original languageEnglish
Article number2104773
JournalAdvanced Science
Issue number11
Publication statusPublished - 2022 Apr 14


  • barristor
  • drop-connected network
  • neuromorphic computing
  • probabilistic synapse
  • silicon
  • silicon oxide

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Chemical Engineering(all)
  • Materials Science(all)
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Engineering(all)
  • Physics and Astronomy(all)


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