Neuromorphic Photonics Based on Phase Change Materials

Neuromorphic photonics devices based on phase change materials (PCMs) and silicon photonics technology have emerged as promising solutions for addressing the limitations of traditional spiking neural networks in terms of scalability, response delay, and energy consumption. In this review, we provide...

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Bibliographic Details
Published in:Nanomaterials (Basel, Switzerland) Switzerland), 2023-05, Vol.13 (11), p.1756
Main Authors: Li, Tiantian, Li, Yijie, Wang, Yuteng, Liu, Yuxin, Liu, Yumeng, Wang, Zhan, Miao, Ruixia, Han, Dongdong, Hui, Zhanqiang, Li, Wei
Format: Article
Language:English
Subjects:
Artificial intelligence
Chemistry
Efficiency
Energy consumption
Firing pattern
Information processing
Insertion loss
Machine learning
Materials Science
Memory
Neural networks
Neuromorphic computing
neuromorphic photonics
Neurons
Optical properties
Optics
Optoelectronics
Phase change materials
Phase transitions
Photonics
Physics
Power consumption
Product development
R&D
Research & development
Review
Science & Technology - Other Topics
Silicon
Silicon nitride
silicon photonics
Spiking
Wave division multiplexing
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Summary:Neuromorphic photonics devices based on phase change materials (PCMs) and silicon photonics technology have emerged as promising solutions for addressing the limitations of traditional spiking neural networks in terms of scalability, response delay, and energy consumption. In this review, we provide a comprehensive analysis of various PCMs used in neuromorphic devices, comparing their optical properties and discussing their applications. We explore materials such as GST (Ge Sb Te ), GeTe-Sb Te , GSST (Ge Sb Se Te ), Sb S /Sb Se , Sc Sb Te (SST), and In Se , highlighting their advantages and challenges in terms of erasure power consumption, response rate, material lifetime, and on-chip insertion loss. By investigating the integration of different PCMs with silicon-based optoelectronics, this review aims to identify potential breakthroughs in computational performance and scalability of photonic spiking neural networks. Further research and development are essential to optimize these materials and overcome their limitations, paving the way for more efficient and high-performance photonic neuromorphic devices in artificial intelligence and high-performance computing applications.
ISSN:2079-4991
2079-4991
DOI:10.3390/nano13111756