Self‐Curable Synaptic Ferroelectric FET Arrays for Neuromorphic Convolutional Neural Network

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...

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Bibliographic Details
Published in:Advanced science 2023-05, Vol.10 (15), p.e2207661-n/a
Main Authors: Shin, Wonjun, Im, Jiyong, Koo, Ryun‐Han, Kim, Jaehyeon, Kwon, Ki‐Ryun, Kwon, Dongseok, Kim, Jae‐Joon, Lee, Jong‐Ho, Kwon, Daewoong
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Behavior
Data processing
Efficiency
Energy consumption
Ferroelectrics
Information storage
in‐memory‐computing
low‐frequency noise (LFN)
Machine learning
Neural networks
Neurons
selective detection
Transistors
tungsten oxide
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Summary: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. The primary challenge that ferroelectric field‐effect transistors face is their vulnerability to the repeated program/erase cycle. To solve this issue, an efficient self‐curing method is presented. The proposed method successfully recovers synaptic fatigue damage, enhancing learning accuracy in the convolutional neural network.
ISSN:2198-3844
2198-3844
DOI:10.1002/advs.202207661