Virtual Production Line for Solar Cell Processing

The importance of semiconductor industries necessitates a corresponding focus on developing students to advance the industry. It is recognized that there is a difficulty in transitioning new approaches from the laboratory to fabrication (lab-to-fab). A production environment has extensive inter-relation between different processes. Moreover, the success of a production environment relies on concepts of yield and optimization of a process for large-scale production, which are not typically introduced or used in most university research environments. Further, the lack of access to large-scale data means that the use of statistical design tools is done using either "made-up" data or examples from other industries. Important, students cannot design an experiment in the courses, testing their statistical design or understanding by changing parameters and examining the outputs. A virtual production line for solar cells, initially developed by UNSW and with Jeff Cotter, enables complex processing to be taught via simulatoins.

Authors

Institutions

Publication Details

Published
2025-06-08
DOI
https://doi.org/10.1109/pvsc59419.2025.11132879
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Virtual Production Line for Solar Cell Processing

Stuart Bowden, Stephen M. Goodnick, Nicholas Rolston, Richard R. King et al.
Industrial Vision Systems and Defect Detection
article

Virtual Production Line for Solar Cell Processing

Stuart Bowden, Stephen M. Goodnick, Nicholas Rolston, Richard R. King, Michael Goryll, Christiana B. Honsberg, J.E. Cotter
article en

Abstract

The importance of semiconductor industries necessitates a corresponding focus on developing students to advance the industry. It is recognized that there is a difficulty in transitioning new approaches from the laboratory to fabrication (lab-to-fab). A production environment has extensive inter-relation between different processes. Moreover, the success of a production environment relies on concepts of yield and optimization of a process for large-scale production, which are not typically introduced or used in most university research environments. Further, the lack of access to large-scale data means that the use of statistical design tools is done using either "made-up" data or examples from other industries. Important, students cannot design an experiment in the courses, testing their statistical design or understanding by changing parameters and examining the outputs. A virtual production line for solar cells, initially developed by UNSW and with Jeff Cotter, enables complex processing to be taught via simulatoins.

Arizona State University (US)
Affordable and clean energy
Openalex Percentile: Top 68%
Industrial Vision Systems and Defect Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Virtual Production Line for Solar Cell Processing — Stuart Bowden, Stephen M. Goodnick, et al. · (2025) | TGRS Research Map | TGRS