MoS2 Synapses with Ultra-low Variability and Their Implementation in Boolean Logic

Adithi Krishnaprasad, Durjoy Dev, Sang Sub Han, Yaqing Shen, Hee-Suk Chung, Tae-Sung Bae, Changhyeon Yoo, Yeonwoong Jung, Mario Lanza, Tania Roy

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Brain-inspired computing enabled by memristors has gained prominence over the years due to the nanoscale footprint and reduced complexity for implementing synapses and neurons. The demonstration of complex neuromorphic circuits using conventional materials systems has been limited by high cycle-to-cycle and device-to-device variability. Two-dimensional (2D) materials have been used to realize transparent, flexible, ultra-thin memristive synapses for neuromorphic computing, but with limited knowledge on the statistical variation of devices. In this work, we demonstrate ultra-low-variability synapses using chemical vapor deposited 2D MoS2 as the switching medium with Ti/Au electrodes. These devices, fabricated using a transfer-free process, exhibit ultra-low variability in SET voltage, RESET power distribution, and synaptic weight update characteristics. This ultra-low variability is enabled by the interface rendered by a Ti/Au top contact on Si-rich MoS2 layers of mixed orientation, corroborated by transmission electron microscopy (TEM), electron energy loss spectroscopy (EELS), and X-ray photoelectron spectroscopy (XPS). TEM images further confirm the stability of the device stack even after subjecting the device to 100 SET-RESET cycles. Additionally, we implement logic gates by monolithic integration of MoS2 synapses with MoS2 leaky integrate-and-fire neurons to show the viability of these devices for non-von Neumann computing.
Original languageEnglish (US)
Pages (from-to)2866-2876
Number of pages11
JournalACS Nano
Volume16
Issue number2
DOIs
StatePublished - Feb 10 2022

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Materials Science(all)
  • Engineering(all)

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