Department of Electrical and Computer Engineering, Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea.
Department of Electronic Engineering, Hanyang University, Seoul, 04763, South Korea.
Adv Sci (Weinh). 2023 May;10(15):e2207661. doi: 10.1002/advs.202207661. Epub 2023 Mar 27.
With the recently increasing prevalence of deep learning, both academia and industry exhibit substantial interest in neuromorphic computing, which mimics the functional and structural features of the human brain. To realize neuromorphic computing, an energy-efficient and reliable artificial synapse must be developed. In this study, the synaptic ferroelectric field-effect-transistor (FeFET) array is fabricated as a component of a neuromorphic convolutional neural network. Beyond the single transistor level, the long-term potentiation and depression of synaptic weights are achieved at the array level, and a successful program-inhibiting operation is demonstrated in the synaptic array, achieving a learning accuracy of 79.84% on the Canadian Institute for Advanced Research (CIFAR)-10 dataset. Furthermore, an efficient self-curing method is proposed to improve the endurance of the FeFET array by tenfold, utilizing the punch-through current inherent to the device. Low-frequency noise spectroscopy is employed to quantitatively evaluate the curing efficiency of the proposed self-curing method. The results of this study provide a method to fabricate and operate reliable synaptic FeFET arrays, thereby paving the way for further development of ferroelectric-based neuromorphic computing.
随着深度学习的最近日益普及，学术界和工业界都对神经形态计算表现出了浓厚的兴趣，神经形态计算模仿了人脑的功能和结构特征。为了实现神经形态计算，必须开发出一种节能且可靠的人工突触。在这项研究中，突触铁电场效应晶体管 (FeFET) 阵列被制作为神经形态卷积神经网络的一个组成部分。超越单个晶体管的水平，在阵列级别实现了突触权重的长期增强和削弱，并且在突触阵列中成功演示了程序抑制操作，在加拿大高级研究所 (CIFAR)-10 数据集上实现了 79.84%的学习精度。此外，提出了一种有效的自修复方法，利用器件固有的穿通电流将 FeFET 阵列的耐久性提高了十倍。利用低频噪声光谱对所提出的自修复方法的修复效率进行了定量评估。这项研究的结果为制造和操作可靠的突触 FeFET 阵列提供了一种方法，从而为基于铁电的神经形态计算的进一步发展铺平了道路。
