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Updated: Jun 25, 2026

Optimization, Test and Diagnostics of Miniaturized Hall Thrusters

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Published on: February 16, 2019

Machine Learning-Enabled In Situ Diagnostics for Intelligent Plasma-Based Semiconductor Manufacturing: A Review.

Minji Kang^1,2, Seongho Kim^1,2, Eunseo Go^1,2

¹Semiconductor Manufacturing Research Center, Korea Institute of Machinery & Materials (KIMM), Daejeon 34103, Republic of Korea.

ACS Applied Materials & Interfaces

|June 24, 2026

Summary

This summary is machine-generated.

Machine learning (ML) enhances semiconductor manufacturing by providing intelligent diagnostics for plasma processes. This technology enables real-time decision-making, process optimization, and fault detection, paving the way for autonomous manufacturing.

Keywords:

in situ plasma diagnostics intelligent semiconductor manufacturing multimodal sensor fusion predictive maintenance uncertainty-aware modeling

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Published on: October 1, 2007

Area of Science:

Materials Science
Chemical Engineering
Computer Science

Background:

Semiconductor manufacturing complexity and tighter process tolerances necessitate advanced control strategies.
Traditional experience-driven methods are insufficient for modern plasma-based processes.
Machine learning (ML) offers a powerful approach to interpret complex plasma diagnostics.

Purpose of the Study:

To review recent advancements in ML for intelligent diagnostics in plasma-based semiconductor manufacturing.
To analyze ML applications from both equipment and ML-focused perspectives.
To identify challenges and propose future directions for AI in plasma processing.

Main Methods:

Review of recent studies on ML applications in plasma-enhanced chemical vapor deposition, reactive ion etching, and sputtering.
Classification of ML applications including anomaly detection, plasma diagnostics, and predictive maintenance.
Examination of emerging strategies like physics-informed learning and explainable AI.

Main Results:

ML models successfully capture nonlinear plasma behavior and enable virtual metrology.
ML facilitates early fault detection, process optimization, and noninvasive monitoring.
Significant progress has been made in real-time data analysis and in situ diagnostics.

Conclusions:

ML is transforming semiconductor plasma processing, enabling intelligent diagnostics and real-time decision-making.
Barriers such as data availability and model interpretability need addressing.
Emerging strategies and a technological roadmap are crucial for industrial readiness and autonomous manufacturing.