A study of conductance update method for Ni/SiNx/Si analog synaptic device. (September 2020)

• Record Type: Journal Article
• Authors: Kim, Boram; Choi, Hyun-Seok; Kim, Yoon
• Is Part Of: Solid-state electronics. Volume 171(2020)
• Publication Date: 2020-09
• Subjects: Resistive random-access memory (RRAM) -- Silicon nitride (Si3N4) -- MIS (Metal-Insulator-Semiconductor) RRAM -- MNIST (Modified National Institute of Standards and Technology database); Semiconductors -- Periodicals; Semiconducteurs -- Périodiques; 621.38152
• DOI: 10.1016/j.sse.2020.107772
• Languages: English
• ISSNs: 0038-1101

Abstract

Highlights: Device-to-system simulation for neuromorphic system was carried out. We proposed Occasional RESET method that can solve the problem of the abrupt RESET phenomenon of RRAM-based synaptic device. We proposed Occasional RESET without re-write method that can overcome drawbacks in terms of time and energy consumption. Abstract: Neuromorphic systems are expected to be a breakthrough beyond the conventional von Neumann architecture when implementing an artificial neural network. In a neuromorphic system, analog synaptic devices store the synaptic weight values of an artificial neural network. Among various memory devices, RRAM-based synaptic device has several advantages such as excellent scaling potential with a simple two-terminal structure and low energy consumption during the read and write operations. However, it has an inherent limitation of abrupt and nonlinear change in the conductance characteristics. Here, we investigate the non-ideal characteristics of conductance modulation using a fabricated RRAM device. We also analyze the impact of non-ideal conductance modulation on pattern recognition accuracy through a device-to-system level simulation. In addition, to solve the drawback of the previous conductance update method (occasional RESET), we propose a new conductance update method (occasional RESET without re-write). This comprehensive experiment and device-to-system level study can facilitate the realization of reliable learning performance on RRAM-based neuromorphic systems.