ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023
Self-Curable Synaptic Ferroelectric FET Arrays for Neuromorphic Convolutional Neural Network.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Charge-Encoded Sidechains Enable Deterministic Ion Ingress and Memory Retention in Organic Electrochemical Synaptic Transistors.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Sustainable Synaptic Device with Two-Dimensional Ferroelectric Materials for Neuromorphic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Neuromorphic Near-Sensor and In-Sensor Computing Enabled by Next-Generation Material-Based Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Hafnium-Based Ferroelectric Post-Moore Electronics: Device Physics, Integration Architectures, and Neuromorphic System Implementation.Nano-micro letters · 2026Review
- Recent advances in ferroelectric materials, devices, and in-memory computing applications.Nano convergence · 2025Review
- Heterojunction-Driven Stochasticity: Bi-Heterojunction Noise-Enhanced Negative Transconductance Transistor in Image Generation.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Ferroelectric NAND for efficient hardware bayesian neural networks.Nature communications · 2025Article
- Low-Frequency Noise Spectroscopy for Navigating Geometrically Varying Strain Effects in HfOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A Sliding-Kernel Computation-In-Memory Architecture for Convolutional Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Low-frequency noise analysis on asymmetric damage and self-recovery behaviors of ZnSnO thin-film transistors under hot carrier stress.Discover nano · 2024Article
- Low-frequency noise of MoTeDiscover nano · 2024Article
- Polarization Pruning: Reliability Enhancement of Hafnia-Based Ferroelectric Devices for Memory and Neuromorphic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Low-Temperature Nanosecond Laser Process of HZO-IGZO FeFETs toward Monolithic 3D System on Chip Integration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Electrolyte-Gated Transistor Array (20 × 20) with Low-Programming Interference Based on Coplanar Gate Structure for Unsupervised Learning.Small science · 2024Article
- Self-Curable Synaptic Ferroelectric FET Arrays for Neuromorphic Convolutional Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
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.
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