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Organic multilevel (opto)electronic memories towards neuromorphic applications
Lin He1, Zuchong Yang2, Zhiming Wang1
1Institute of Fundamental and Frontier Sciences (IFFS), University of Electronic Science and Technology of China, Chengdu 610054, China. tim_leydecker@uestc.edu.cn.
Nanoscale
|June 28, 2023
Summary
Organic multilevel memory devices show promise for neuromorphic computing, overcoming limitations of traditional architectures. This review highlights advancements in organic materials for efficient synaptic weight operations.
Area of Science:
- Materials Science
- Computer Engineering
- Neuroscience
Background:
- Neuromorphic computing aims to overcome the von Neumann bottleneck.
- Organic materials offer tunable properties for advanced electronic devices.
- Multilevel memory is crucial for mimicking synaptic weight in neuromorphic systems.
Purpose of the Study:
- To review recent advancements in organic multilevel memory for neuromorphic applications.
- To discuss operating principles and achievements of various organic multilevel memory approaches.
- To explore the use of organic multilevel memories in neuromorphic circuits.
Main Methods:
- Review of recent scientific literature on organic multilevel memory devices.
- Analysis of devices utilizing floating gates, ferroelectric materials, polymer electrets, and photochromic molecules.
- Discussion of performance metrics and integration into neuromorphic circuits.
Main Results:
- Organic multilevel memories demonstrate potential for synaptic weight emulation.
- Key approaches like floating gates and ferroelectric materials show significant progress.
- Organic materials offer advantages in tunability and fabrication for neuromorphic hardware.
Conclusions:
- Organic multilevel memory is a promising technology for efficient neuromorphic computing.
- Further research is needed to address the advantages and drawbacks of organic materials in this field.
- These devices could lead to more powerful and energy-efficient artificial intelligence systems.
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