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...
Saved in:
Main Authors: | , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2024-11-01
|
Series: | Advanced Science |
Subjects: | |
Online Access: | https://doi.org/10.1002/advs.202407870 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
_version_ | 1846151474721062912 |
---|---|
author | Jangsaeng Kim Eun Chan Park Wonjun Shin Ryun‐Han Koo Jiseong Im Chang‐Hyeon Han Jong‐Ho Lee Daewoong Kwon |
author_facet | Jangsaeng Kim Eun Chan Park Wonjun Shin Ryun‐Han Koo Jiseong Im Chang‐Hyeon Han Jong‐Ho Lee Daewoong Kwon |
author_sort | Jangsaeng Kim |
collection | DOAJ |
description | 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. |
format | Article |
id | doaj-art-87be31e5dfb444b1941ab9b098b3838c |
institution | Kabale University |
issn | 2198-3844 |
language | English |
publishDate | 2024-11-01 |
publisher | Wiley |
record_format | Article |
series | Advanced Science |
spelling | doaj-art-87be31e5dfb444b1941ab9b098b3838c2024-11-27T11:21:53ZengWileyAdvanced Science2198-38442024-11-011144n/an/a10.1002/advs.202407870All‐Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary NeuronsJangsaeng Kim0Eun Chan Park1Wonjun Shin2Ryun‐Han Koo3Jiseong Im4Chang‐Hyeon Han5Jong‐Ho Lee6Daewoong Kwon7Department of Electronic Engineering Hanyang University Seoul 04763 Republic of KoreaDepartment of Electronic Engineering Hanyang University Seoul 04763 Republic of KoreaDepartment of Semiconductor Convergence Engineering Sungkyunkwan University Suwon 16419 Republic of KoreaDepartment of Electrical and Computer Engineering and Inter‐university Semiconductor Research Center Seoul National University Seoul 08826 Republic of KoreaDepartment of Electrical and Computer Engineering and Inter‐university Semiconductor Research Center Seoul National University Seoul 08826 Republic of KoreaDepartment of Electronic Engineering Hanyang University Seoul 04763 Republic of KoreaDepartment of Electrical and Computer Engineering and Inter‐university Semiconductor Research Center Seoul National University Seoul 08826 Republic of KoreaDepartment of Electronic Engineering Hanyang University Seoul 04763 Republic of KoreaAbstract 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.https://doi.org/10.1002/advs.202407870double‐gateleaky integrate‐and‐fire neuronmorphotropic phase boundaryneuromorphicspike‐frequency adaptationspiking neural networks |
spellingShingle | Jangsaeng Kim Eun Chan Park Wonjun Shin Ryun‐Han Koo Jiseong Im Chang‐Hyeon Han Jong‐Ho Lee Daewoong Kwon All‐Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary Neurons Advanced Science double‐gate leaky integrate‐and‐fire neuron morphotropic phase boundary neuromorphic spike‐frequency adaptation spiking neural networks |
title | All‐Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary Neurons |
title_full | All‐Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary Neurons |
title_fullStr | All‐Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary Neurons |
title_full_unstemmed | All‐Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary Neurons |
title_short | All‐Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary Neurons |
title_sort | all ferroelectric spiking neural networks via morphotropic phase boundary neurons |
topic | double‐gate leaky integrate‐and‐fire neuron morphotropic phase boundary neuromorphic spike‐frequency adaptation spiking neural networks |
url | https://doi.org/10.1002/advs.202407870 |
work_keys_str_mv | AT jangsaengkim allferroelectricspikingneuralnetworksviamorphotropicphaseboundaryneurons AT eunchanpark allferroelectricspikingneuralnetworksviamorphotropicphaseboundaryneurons AT wonjunshin allferroelectricspikingneuralnetworksviamorphotropicphaseboundaryneurons AT ryunhankoo allferroelectricspikingneuralnetworksviamorphotropicphaseboundaryneurons AT jiseongim allferroelectricspikingneuralnetworksviamorphotropicphaseboundaryneurons AT changhyeonhan allferroelectricspikingneuralnetworksviamorphotropicphaseboundaryneurons AT jongholee allferroelectricspikingneuralnetworksviamorphotropicphaseboundaryneurons AT daewoongkwon allferroelectricspikingneuralnetworksviamorphotropicphaseboundaryneurons |