All‐Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary Neurons

All‐Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary Neurons

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 re...

Full description

Saved in:
Bibliographic Details
Main Authors: Jangsaeng Kim, Eun Chan Park, Wonjun Shin, Ryun‐Han Koo, Jiseong Im, Chang‐Hyeon Han, Jong‐Ho Lee, Daewoong Kwon
Format: Article
Language:English
Published: Wiley 2024-11-01
Series:Advanced Science
Subjects:
double‐gate
leaky integrate‐and‐fire neuron
morphotropic phase boundary
neuromorphic
spike‐frequency adaptation
spiking neural networks
Online Access:https://doi.org/10.1002/advs.202407870
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2198-3844

Similar Items