Reconfigurable MoS2Memtransistors for Continuous Learning in Spiking Neural Networks

Jiangtan Yuan, Stephanie E. Liu, Ahish Shylendra, William A. Gaviria Rojas, Silu Guo, Hadallia Bergeron, Shaowei Li, Hong Sub Lee, Shamma Nasrin, Vinod K. Sangwan, Amit Ranjan Trivedi, Mark C. Hersam*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Artificial intelligence and machine learning are growing computing paradigms, but current algorithms incur undesirable energy costs on conventional hardware platforms, thus motivating the exploration of more efficient neuromorphic architectures. Toward this end, we introduce here a memtransistor with gate-tunable dynamic learning behavior. By fabricating memtransistors from monolayer MoS2 grown on sapphire, the relative importance of the vertical field effect from the gate is enhanced, thereby heightening reconfigurability of the device response. Inspired by biological systems, gate pulses are used to modulate potentiation and depression, resulting in diverse learning curves and simplified spike-timing-dependent plasticity that facilitate unsupervised learning in simulated spiking neural networks. This capability also enables continuous learning, which is a previously underexplored cognitive concept in neuromorphic computing. Overall, this work demonstrates that the reconfigurability of memtransistors provides unique hardware accelerator opportunities for energy efficient artificial intelligence and machine learning.

Original languageEnglish (US)
Pages (from-to)6432-6440
Number of pages9
JournalNano letters
Issue number15
StatePublished - Aug 11 2021


  • 2D materials
  • artificial intelligence
  • hardware accelerator
  • machine learning
  • neuromorphic computing

ASJC Scopus subject areas

  • Bioengineering
  • Chemistry(all)
  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanical Engineering


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