Details
| Original language | English |
|---|---|
| Title of host publication | 2021 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2021 |
| Publisher | IEEE Computer Society |
| Pages | 51-52 |
| Number of pages | 2 |
| ISBN (electronic) | 9781665412766 |
| ISBN (print) | 978-1-6654-4836-9 |
| Publication status | Published - 13 Sept 2021 |
| Event | 2021 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2021 - Turin, Italy Duration: 13 Sept 2021 → 17 Sept 2021 |
Publication series
| Name | Proceedings of the International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD |
|---|---|
| Volume | 2021-September |
| ISSN (Print) | 2158-3234 |
| ISSN (electronic) | 2158-3242 |
Abstract
We present a methodology where we combine numerical TCAD device modeling, machine learning and advanced statistics for getting a deeper understanding of how process variations influence device performance in mass produced crystalline silicon solar cells. For this, we use seven model input parameters that affect the mainstream solar cell design (PERC) and its performance the most and perform about a couple of hundred numerical TCAD device simulations in an expected range of these parameters. As such detailed numerical simulations take long time, we train and validate machine learning models on these simulations, which serve to describe ten thousands of fabricated PERC cells. The method gives concrete information for improving PERC cells with a modest amount of numerical modeling and hence in a very short time. This approach is not limited to a specific solar cell design or product.
Keywords
- machine learning, mass-production, PERC, Solar Cells, TCAD
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
- Mathematics(all)
- Modelling and Simulation
Cite this
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2021 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2021. IEEE Computer Society, 2021. p. 51-52 (Proceedings of the International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD; Vol. 2021-September).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Machine Learning for Optimization of Mass-Produced Industrial Silicon Solar Cells
AU - Wagner-Mohnsen, Hannes
AU - Altermatt, Pietro P.
PY - 2021/9/13
Y1 - 2021/9/13
N2 - We present a methodology where we combine numerical TCAD device modeling, machine learning and advanced statistics for getting a deeper understanding of how process variations influence device performance in mass produced crystalline silicon solar cells. For this, we use seven model input parameters that affect the mainstream solar cell design (PERC) and its performance the most and perform about a couple of hundred numerical TCAD device simulations in an expected range of these parameters. As such detailed numerical simulations take long time, we train and validate machine learning models on these simulations, which serve to describe ten thousands of fabricated PERC cells. The method gives concrete information for improving PERC cells with a modest amount of numerical modeling and hence in a very short time. This approach is not limited to a specific solar cell design or product.
AB - We present a methodology where we combine numerical TCAD device modeling, machine learning and advanced statistics for getting a deeper understanding of how process variations influence device performance in mass produced crystalline silicon solar cells. For this, we use seven model input parameters that affect the mainstream solar cell design (PERC) and its performance the most and perform about a couple of hundred numerical TCAD device simulations in an expected range of these parameters. As such detailed numerical simulations take long time, we train and validate machine learning models on these simulations, which serve to describe ten thousands of fabricated PERC cells. The method gives concrete information for improving PERC cells with a modest amount of numerical modeling and hence in a very short time. This approach is not limited to a specific solar cell design or product.
KW - machine learning
KW - mass-production
KW - PERC
KW - Solar Cells
KW - TCAD
UR - http://www.scopus.com/inward/record.url?scp=85116309110&partnerID=8YFLogxK
U2 - 10.1109/NUSOD52207.2021.9541457
DO - 10.1109/NUSOD52207.2021.9541457
M3 - Conference contribution
AN - SCOPUS:85116309110
SN - 978-1-6654-4836-9
T3 - Proceedings of the International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD
SP - 51
EP - 52
BT - 2021 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2021
PB - IEEE Computer Society
T2 - 2021 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2021
Y2 - 13 September 2021 through 17 September 2021
ER -