ArticleNature communications2023
Highly-scaled and fully-integrated 3-dimensional ferroelectric transistor array for hardware implementation of neural networks.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Ferroelectric Transistors: from Materials Innovation to Intelligent Electronic Systems.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Charge-Encoded Sidechains Enable Deterministic Ion Ingress and Memory Retention in Organic Electrochemical Synaptic Transistors.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Hybrid ferroelectric-ionic memristive hardware for high scalability in-memory computing.Nature communications · 2026Article
- Recent progress in HfONanoscale advances · 2026Review
- Advances and Perspectives in Graphene-Based Quantum Dots Enabled Neuromorphic Devices.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Ferroelectrics Hybrids: Harnessing Multifunctionality of 2D Semiconductors in the Post-Moore Era.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- High-throughput design of optoelectronic-ferroelectric heterostructure from materials to sensor-memory-computing devices.National science review · 2026Article
- Challenges and Opportunities of Upconversion Nanoparticles for Emerging NIR Optoelectronic Devices.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Recent advances in ferroelectric materials, devices, and in-memory computing applications.Nano convergence · 2025Review
- Ferroelectric NAND for efficient hardware bayesian neural networks.Nature communications · 2025Article
- Memristive Bellman solver for decision-making.Nature communications · 2025Article
- Memristor-Based Artificial Neural Networks for Hardware Neuromorphic Computing.Research (Washington, D.C.) · 2025Review
- An Antagonistic Photovoltaic Memristor for Bioinspired Active Contrast Adaptation.Advanced materials (Deerfield Beach, Fla.) · 2024Article
- A Near-Infrared Retinomorphic Device with High Dimensionality Reservoir Expression.Advanced materials (Deerfield Beach, Fla.) · 2024Article
- Unlocking large memory windows and 16-level data per cell memory operations in hafnia-based ferroelectric transistors.Science advances · 2024Article
- Well-defined in-textile photolithography towards permeable textile electronics.Nature communications · 2024Article
- A comprehensive review of advanced trends: from artificial synapses to neuromorphic systems with consideration of non-ideal effects.Frontiers in neuroscience · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Hardware-based neural networks (NNs) can provide a significant breakthrough in artificial intelligence applications due to their ability to extract features from unstructured data and learn from them. However, realizing complex NN models remains challenging because different tasks, such as feature extraction and classification, should be performed at different memory elements and arrays. This further increases the required number of memory arrays and chip size. Here, we propose a three-dimensional ferroelectric NAND (3D FeNAND) array for the area-efficient hardware implementation of NNs. Vector-matrix multiplication is successfully demonstrated using the integrated 3D FeNAND arrays, and excellent pattern classification is achieved. By allocating each array of vertical layers in 3D FeNAND as the hidden layer of NN, each layer can be used to perform different tasks, and the classification of color-mixed patterns is achieved. This work provides a practical strategy to realize high-performance and highly efficient NN systems by stacking computation components vertically.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.