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A Standard and Reliable Method to Fabricate Two-Dimensional Nanoelectronics

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Artificial Intelligence-Driven Approaches in Semiconductor Research.

Yiqiang Zheng¹, Hao Xu¹, Zhexin Li¹

¹State Key Laboratory of Semiconductor Physics and Chip Technologies, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China.

Advanced Materials (Deerfield Beach, Fla.)

|June 19, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) accelerates semiconductor development by analyzing vast datasets for materials discovery, synthesis, and device optimization. Future research should address AI challenges for advanced semiconductor applications.

Keywords:

artificial intelligence autonomous laboratory high‐throughput machine learning semiconductors

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Area of Science:

Materials Science
Computer Science
Electrical Engineering

Background:

Integrated circuits face scaling and power consumption challenges, driving research into novel semiconductor materials and device architectures.
Exploring vast material and parameter spaces is costly and inefficient with traditional methods.
Artificial intelligence (AI) offers a promising solution for accelerating semiconductor technology development.

Purpose of the Study:

To provide a comprehensive overview of AI applications in semiconductor research.
To highlight AI's role in accelerating materials discovery, screening, synthesis, characterization, and device optimization.
To discuss the integration of AI for understanding the processing-structure-property-performance (PSPP) relationship.

Main Methods:

Review of current AI-driven approaches in semiconductor research.
Analysis of AI's impact across the entire research pipeline.
Identification of challenges and future research directions.

Main Results:

AI significantly aids in analyzing large datasets, improving efficiency in semiconductor research.
AI facilitates a holistic understanding of the PSPP relationship.
AI assists in materials discovery, screening, synthesis, characterization, and device optimization.

Conclusions:

AI is a powerful tool for advancing semiconductor technology, offering advantages in data analysis and research acceleration.
Challenges remain in dataset quality, model generalizability, and autonomous experimentation.
Collaborative efforts are crucial for overcoming AI challenges and advancing semiconductor research, particularly in areas like wafer-scale growth of high-quality thin films.