A Multi-Institutional Digital Twin and AI Educational Platform for Advanced Microelectronics Fabrication and Device Packaging Training

Technology today is at a crossroad between Industry 4.0 and 5.0, where products and services are manufactured and designed to be ‘human-centered’ and sustainable. Such objectives have always been expected in education, especially in the way the workforce is trained. Leveraging on prior research which was done by a team from Pasadena City College (PCC) and UC Irvine, we have formed a bi-coastal collaborative, including Mercer County Community College (MCCC) and Princeton University, to expand and transcend our earlier AI-powered virtual reality (VR) simulation framework and platform, AI-powered digital twin (DT/AI) for education. This Phase-2 effort demonstrates a working, multi-institution R&D platform with the following capabilities: (a) new training modules with enhanced lithography training and advanced device packaging, (b) better optimized, high-fidelity equipment emulations and process simulations that much closely replicate the physical equipment, (c) adherence to documented facility-specific Standard Operation Procedure (SOP) and manufacturing process flow, (d) a parallel fabricated chip-under-test and its test board for I/O connectivity verification, and last but not the least, (e) a tightly-coupled agentic AI engines available throughout the training session to customize learner-centric experiences to enhance individual knowledge acquisition and retention. The authors also continue to demonstrate the feasibility and scalability benefits of foundational VR/AR-based training for students, technicians, and up-skill learners for whom direct cleanroom or packaging lab access is often unattainable; while with DT/AI, learning and feedback is affordable and widely accessible.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-05
DOI
https://doi.org/10.5281/zenodo.23171520
Primary Topic
Experimental Learning in Engineering
Type
article
Field-Weighted Citation Impact
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article

A Multi-Institutional Digital Twin and AI Educational Platform for Advanced Microelectronics Fabrication and Device Packaging Training

B. Harrop, Kristal Hong, Ishan Jha, Abubaker Ahmadi et al.
Zenodo (CERN European Organization for Nuclear Research)
Experimental Learning in Engineering
article

A Multi-Institutional Digital Twin and AI Educational Platform for Advanced Microelectronics Fabrication and Device Packaging Training

B. Harrop, Kristal Hong, Ishan Jha, Abubaker Ahmadi, Hanumath Mandadi, Shiannling Wu, Ruben Melara, Ryan Almache, Rishav Dhar, Peter Stout, Alex Norman, G.P. Li
article en

Abstract

Technology today is at a crossroad between Industry 4.0 and 5.0, where products and services are manufactured and designed to be ‘human-centered’ and sustainable. Such objectives have always been expected in education, especially in the way the workforce is trained. Leveraging on prior research which was done by a team from Pasadena City College (PCC) and UC Irvine, we have formed a bi-coastal collaborative, including Mercer County Community College (MCCC) and Princeton University, to expand and transcend our earlier AI-powered virtual reality (VR) simulation framework and platform, AI-powered digital twin (DT/AI) for education. This Phase-2 effort demonstrates a working, multi-institution R&D platform with the following capabilities: (a) new training modules with enhanced lithography training and advanced device packaging, (b) better optimized, high-fidelity equipment emulations and process simulations that much closely replicate the physical equipment, (c) adherence to documented facility-specific Standard Operation Procedure (SOP) and manufacturing process flow, (d) a parallel fabricated chip-under-test and its test board for I/O connectivity verification, and last but not the least, (e) a tightly-coupled agentic AI engines available throughout the training session to customize learner-centric experiences to enhance individual knowledge acquisition and retention. The authors also continue to demonstrate the feasibility and scalability benefits of foundational VR/AR-based training for students, technicians, and up-skill learners for whom direct cleanroom or packaging lab access is often unattainable; while with DT/AI, learning and feedback is affordable and widely accessible.

Zenodo (CERN European Organization for Nuclear Research)
Princeton University (US), University of California, Irvine (US), Mercer County Community College (US)
Openalex Percentile: Top 12%
Experimental Learning in Engineering
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