Recent advances in neuromorphic transistors for artificial perception applications: FOCUS ISSUE REVIEW

Area of Science:

• Materials Science
• Computer Engineering
• Neuroscience

Background:

• Conventional von Neumann architecture faces challenges in energy efficiency, in-memory computing, and dynamic learning for artificial intelligence (AI).
• Neuromorphic computing presents a novel paradigm to address these limitations.
• Neuromorphic devices exhibiting synaptic plasticity are crucial for developing advanced neuromorphic systems.

Purpose of the Study:

• This review discusses the operation, function, and applications of three-terminal neuromorphic transistors.
• It highlights recent advancements in artificial perception systems utilizing these transistors.
• The study aims to summarize opportunities and challenges in artificial perception systems.

Main Methods:

• Review of existing literature on neuromorphic transistors and their applications.
• Analysis of the mechanisms and functions of neuromorphic transistors.
• Synthesis of recent progress in artificial perception systems based on neuromorphic transistors.

Main Results:

• Neuromorphic transistors demonstrate potential for energy-efficient AI and in-memory computing.
• These transistors are key components for realizing synaptic plasticity and neuromorphic functions.
• Progress has been made in developing artificial perception systems inspired by human senses.

Conclusions:

• Neuromorphic transistors are pivotal for overcoming conventional computing limitations in AI.
• Artificial perception systems based on neuromorphic transistors offer new possibilities for intelligent robots.
• Further research is needed to address the opportunities and challenges in this field.