A printing-inspired digital twin for the self-driving, high-throughput, closed-loop optimization of roll-to-roll printed photovoltaics

A printing-inspired digital twin for the self-driving, high-throughput, closed-loop optimization of roll-to-roll printed photovoltaics

The quest for sustainable energy has led to significant advancements in photovoltaic (PV) technology. Traditional methods often lag, prompting the development of automated, high-throughput technologies. We introduce the MicroFactory, a self-driving digital twin that revolutionizes roll-to-roll (R2R)...

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Bibliographic Details
Main Authors: Ng, Leonard Wei Tat, An, Na Gyeong, Yang, Liu, Zhou, Yinhua, Chang, Dong Wook, Kim, Jueng-Eun, Sutherland, Luke J., Hasan, Tawfique, Gao, Mei, Vak, Doojin
Other Authors: School of Materials Science and Engineering
Format: Journal Article
Language:English
Published: 2024
Subjects:
Engineering
Machine learning
Printed photovoltaics
Online Access:https://hdl.handle.net/10356/178856

Internet

https://hdl.handle.net/10356/178856

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