ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
All-Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary Neurons.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Ferroelectric Transistors: from Materials Innovation to Intelligent Electronic Systems.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.Nano-micro letters · 2026Review
- Towards Artificial Intelligence Hardware With 3D Integrated Ferroelectric Transistors.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Multi-Physical Field Modulated P-Bit Device Based on VOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Stack-Engineered Mode Selection in PtMn/(Co/Pd)ACS applied materials & interfaces · 2026Article
- Organic Electrochemical Transistors for Neuromorphic Devices and Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Article
- All-Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary Neurons.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Artificial neurons and synapses are crucial for efficiently implementing spiking neural networks (SNNs) in hardware. The distinct functional requirements of artificial neurons and synapses present significant challenges in the implementation of area- and energy-efficient SNNs. This study reports an all-ferroelectric SNN system through co-optimization of material properties and device configurations using wafer-scale atomic layer deposition. For the first time, a double-gate (DG) morphotropic phase boundary-based thin-film transistor (MPBTFT) is utilized for a leaky integrate-and-fire (LIF) neuron. The DG MPBTFT-based LIF neuron eliminates the need for capacitors and reset circuits, thereby enhancing area and energy efficiency. The DG configuration demonstrates various neuronal functions with high reliability. Co-optimizing materials and devices significantly enhance the performance and functional versatility of artificial neurons and synapses. Meticulous material engineering facilitates the seamless co-integration of DG MPBTFT-based neurons, ferroelectric thin-film transistor (TFT)-based synapses, and normal TFTs on a single wafer. All-ferroelectric SNN systems achieved a high classification accuracy of 94.9%, thereby highlighting the potential of DG MPBTFT-based LIF neurons for advanced neuromorphic computing.
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Registered trials
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