(Bi0.2Sb0.8)2Te3 based dynamic synapses with programmable spatio-temporal dynamics

Qingzhou Wan, Peng Zhang, Qiming Shao, Mohammad T. Sharbati, John R. Erickson, Kang L. Wang, Feng Xiong

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Neuromorphic computing has recently emerged as a promising paradigm to overcome the von-Neumann bottleneck and enable orders of magnitude improvement in bandwidth and energy efficiency. However, existing complementary metal-oxide-semiconductor (CMOS) digital devices, the building block of our computing system, are fundamentally different from the analog synapses, the building block of the biological neural network - rendering the hardware implementation of the artificial neural networks (ANNs) not scalable in terms of area and power, with existing CMOS devices. In addition, the spatiotemporal dynamic, a crucial component for cognitive functions in the neural network, has been difficult to replicate with CMOS devices. Here, we present the first topological insulator (TI) based electrochemical synapse with programmable spatiotemporal dynamics, where long-term and short-term plasticity in the TI synapse are achieved through the charge transfer doping and ionic gating effects, respectively. We also demonstrate basic neuronal functions such as potentiation/depression and paired-pulse facilitation with high precision (>500 states per device), as well as a linear and symmetric weight update. We envision that the dynamic TI synapse, which shows promising scaling potential in terms of energy and speed, can lead to the hardware acceleration of truly neurorealistic ANNs with superior cognitive capabilities and excellent energy efficiency.

Original languageEnglish
Article number101107
JournalAPL Materials
Issue number10
Publication statusPublished - 2019 Oct 1

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • General Engineering


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