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R&D: Nanoribbon Device for Analog Phase Change Memory Targeting Neural Network Applications

Work provides effective way to implement gradual RESET for PCM devices.

Applied Physics Letters has published an article written by Xin Li, Ruizhe Zhao, School of Integrated Circuits, Huazhong University of Science & Technology, Wuhan 430074, China, Hao Tong , and Xiangshui Miao, School of Integrated Circuits, Huazhong University of Science & Technologyn Wuhan 430074, China, and Hubei Yangtze Memory Laboratories, Wuhan 430205, China.

Abstract: Phase change memory (PCM) is one of the most mature technologies for non-von Neumann computing. However, abrupt amorphization becomes a barrier for training artificial neural networks, due to limitations of the inherent operational mechanism of phase change materials. The devices can achieve a gradual conductance change in the crystallization process, while the conductance change for amorphization process is much more abrupt. This work presents a possible explanation for the RESET abrupt change issue in T-shaped devices, based on the analysis of the volume and connectivity of the amorphous and crystalline regions. Using this model, a nanoribbon device for analog PCM targeting neural network applications is designed, fabricated, and characterized. The designed device can realize a gradual RESET without changing the amplitude and width of RESET pulses. Using a nanoribbon device as a single synapse in the designed array reduces the number of SET operations needed to achieve the same accuracy in convolutional neural network simulation by 75%, which implies a significant reduction in power and time consumption. This work provides an effective way to implement gradual RESET for PCM devices.

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