Compact model of retention characteristics of ferroelectric FinFET synapse with MFIS gate stack. (21st December 2021)

• Record Type: Journal Article
• Authors: Baig, Md Aftab; Le, Hoang-Hiep; De, Sourav; Chang, Che-Wei; Hsieh, Chia-Chi; Huang, Xiao-Shan; Lee, Yao-Jen; Lu, Darsen D
• Is Part Of: Semiconductor science and technology. Volume 37:Number 2(2022)
• Publication Date: 2021-12-21
• Subjects: neuromorphic devices -- neural networks -- ferroelectric-FinFET -- retention -- refresh circuit -- compact model; Semiconductors -- Periodicals; 621.38152
• DOI: 10.1088/1361-6641/ac3f22
• Languages: English
• ISSNs: 0268-1242

Abstract

Abstract: In this paper, multiple-fin n- and p-channel HfZrO2 ferroelectric-FinFET devices are manufactured using a gate first process with post metalization annealing. The device transfer characteristics upon program and erase operations are measured and modeled. The drift in the transfer characteristics due to depolarization field and charge injection are captured using the shift in the threshold voltage along with time-dependent modeling of vertical field dependent mobility degradation parameters to develop a physical, computationally efficient, and accurate retention model for ferroelectric-FinFET devices. The modeled conductance is incorporated into deep neural network simulation platform CIMulator to analyze the role of conductance drift due to retention degradation, as well as the importance of the gap between high and low conductance states in improving the image recognition accuracy of neural networks.