Adaptive Digital Twin Platform for Training in Semiconductor Cleanroom Processes and Operations

Training the next generation of semiconductor engineers requires scalable, high-fidelity platforms that mirror real-world cleanroom operations. Traditional lab-based training is resource-intensive, limited in accessibility, and often detached from the realities of modern smart manufacturing environments. To address this, we present an AI-powered immersive training platform that leverages digital twin technology and extended reality (XR) to simulate critical semiconductor cleanroom processes—including spin coating, mask alignment, resist development, and gowning.

Our platform is designed for both academic and industrial upskilling applications, offering preceptor and trainee modes. Instructors can guide sessions remotely, intervene in real time, and refine grading criteria through a human-in-the-loop feedback system. Trainees experience step-by-step procedural training and real-time feedback within a fully interactive cleanroom environment. One key innovation is the use of large language models (LLMs) to automatically extract and structure content from expert demonstration videos and textual documentation into modular XR training sequences. This reduces authoring overhead while maintaining expert-level instructional quality.

The system incorporates adaptive learning features that dynamically adjust instructional difficulty based on user performance. For example, a user struggling with alignment precision may receive additional guided support, while advanced users are progressively challenged with efficiency or contamination mitigation goals. This adaptive scaffolding ensures learning outcomes are tailored to individual user capabilities.

To evaluate the platform’s effectiveness, we conducted a controlled user study involving university-level trainees. Participants were assessed on knowledge retention, procedural accuracy, and task completion time before and after XR-based training. The results demonstrated statistically significant improvements in procedural fluency and user confidence compared to traditional instruction methods.

Our approach addresses key challenges in cleanroom access, scalability of instruction, and reproducibility of high-quality training experiences. It allows institutions to reduce dependency on expensive physical labs while increasing training throughput and quality. Furthermore, the digital twin framework allows the system to be easily extended to additional tools and cleanroom configurations, making it ideal for integration into smart manufacturing curricula.

This work contributes a practical, AI-enhanced training solution for microelectronics education and talent development—aligned with the smart manufacturing imperatives of automation, adaptability, and data-informed instruction. By blending immersive learning technologies with intelligent content generation and real-time assessment, this platform helps bridge the gap between education and advanced semiconductor fabrication environments.