Multilevel artificial electronic synaptic device of direct grown robust MoS2 based memristor array for in-memory deep neural network

Muhammad Naqi, Min Seok Kang, Na liu, Taehwan Kim, Seungho Baek, Arindam Bala, Changgyun Moon, Jongsun Park, Sunkook Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

With an increasing demand for artificial intelligence, the emulation of the human brain in neuromorphic computing has led to an extraordinary result in not only simulating synaptic dynamics but also reducing complex circuitry systems and algorithms. In this work, an artificial electronic synaptic device based on a synthesized MoS2 memristor array (4 × 4) is demonstrated; the device can emulate synaptic behavior with the simulation of deep neural network (DNN) learning. MoS2 film is directly synthesized onto a patterned bottom electrode (Pt) with high crystallinity using sputtering and CVD. The proposed MoS2 memristor exhibits excellent memory operations in terms of endurance (up to 500 sweep cycles) and retention (~ 104) with a highly uniform memory performance of crossbar array (4 × 4) up to 16 memristors on a scalable level. Next, the proposed MoS2 memristor is utilized as a synaptic device that demonstrates close linear and clear synaptic functions in terms of potentiation and depression. When providing consecutive multilevel pulses with a defined time width, long-term and short-term memory dynamics are obtained. In addition, an emulation of the artificial neural network of the presented synaptic device showed 98.55% recognition accuracy, which is 1% less than that of software-based neural network emulations. Thus, this work provides an enormous step toward a neural network with a high recognition accuracy rate.

Original languageEnglish
Article number53
Journalnpj 2D Materials and Applications
Volume6
Issue number1
DOIs
Publication statusPublished - 2022 Dec

ASJC Scopus subject areas

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

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