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

• Published in: NPJ 2D materials and applications Vol. 6; no. 1; pp. 1 - 9
• Main Authors: Naqi, Muhammad, Kang, Min Seok, liu, Na, Kim, Taehwan, Baek, Seungho, Bala, Arindam, Moon, Changgyun, Park, Jongsun, Kim, Sunkook
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04.08.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1005/1007; 639/925/927/1007; Algorithms; Arrays; Artificial intelligence; Artificial neural networks; Chemistry and Materials Science; Circuits; Computer memory; Computer simulation; Machine learning; Materials Science; Memristors; Molybdenum disulfide; Nanotechnology; Neural networks; Recognition; Surfaces and Interfaces; Synthesis; Thin Films
• ISSN: 2397-7132; 2397-7132
• DOI: 10.1038/s41699-022-00325-5

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 MoS 2 memristor array (4 × 4) is demonstrated; the device can emulate synaptic behavior with the simulation of deep neural network (DNN) learning. MoS 2 film is directly synthesized onto a patterned bottom electrode (Pt) with high crystallinity using sputtering and CVD. The proposed MoS 2 memristor exhibits excellent memory operations in terms of endurance (up to 500 sweep cycles) and retention (~ 10 4 ) with a highly uniform memory performance of crossbar array (4 × 4) up to 16 memristors on a scalable level. Next, the proposed MoS 2 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.