Machine Learning Algorithm for Efficient Design of Separated Buffer Super-Junction IGBT
Abstract
:1. Introduction
2. Structure and Methodology
2.1. Proposed Structure and Characteristics
2.2. Designing ML Algorithm
3. Model Validation and Results
3.1. Verification of Model Reliability
- µ: mean;
- σ: standard deviation;
- n: number of samples in one set of data.
3.2. Optimization
3.3. Reverse Engineering by NN
4. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | C-SJBT | SB-SJBT |
---|---|---|
Width | 3.0 µm | 3.0 µm |
Length | 50.0 µm | 50.0 µm |
Gate depth | 5.0 µm | 5.0 µm |
p/n-pillar width | 1.5 µm | 1.5 µm |
p/n-side n-buffer width | - | 1.5 µm |
n-buffer layer depth | 9.5 µm | 9.5 µm |
p-base doping | cm−3 | cm−3 |
n-source doping | cm−3 | cm−3 |
p/n-pillar doping | cm−3 | cm−3 |
n-buffer doping | cm−3 | - |
p-collector doping | cm−3 | cm−3 |
Data Set | Eoff [µJ] | Von [V] | BV [V] | ||||
---|---|---|---|---|---|---|---|
µ | σ | µ | σ | µ | σ | ||
TCAD | 1.19 | 6.94 | 1.511 | 0.137 | 629.03 | 8.719 | |
ML results 1 | 1.18 | 6.62 | 1.509 | 0.138 | 628.57 | 8.780 | |
ML results 2 | 1.16 | 6.63 | 1.513 | 0.137 | 629.15 | 8.979 | |
Confidence interval | Max | 1.24 | 7.27 | 1.571 | 0.142 | 629.63 | 10.489 |
Min | 1.14 | 6.60 | 1.451 | 0.132 | 619.99 | 8.295 |
Structure | A | B | C | D |
---|---|---|---|---|
cm−3] | ||||
cm−3] | ||||
Eoff [μJ] | ||||
Von [V] | 1.39 | 1.38 | 1.37 | 1.38 |
BV [V] | 622.4 | 620.5 | 617.2 | 617.1 |
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Kim, K.Y.; Hwang, T.H.; Song, Y.S.; Kim, H.; Kim, J.H. Machine Learning Algorithm for Efficient Design of Separated Buffer Super-Junction IGBT. Micromachines 2023, 14, 334. https://doi.org/10.3390/mi14020334
Kim KY, Hwang TH, Song YS, Kim H, Kim JH. Machine Learning Algorithm for Efficient Design of Separated Buffer Super-Junction IGBT. Micromachines. 2023; 14(2):334. https://doi.org/10.3390/mi14020334
Chicago/Turabian StyleKim, Ki Yeong, Tae Hyun Hwang, Young Suh Song, Hyunwoo Kim, and Jang Hyun Kim. 2023. "Machine Learning Algorithm for Efficient Design of Separated Buffer Super-Junction IGBT" Micromachines 14, no. 2: 334. https://doi.org/10.3390/mi14020334
APA StyleKim, K. Y., Hwang, T. H., Song, Y. S., Kim, H., & Kim, J. H. (2023). Machine Learning Algorithm for Efficient Design of Separated Buffer Super-Junction IGBT. Micromachines, 14(2), 334. https://doi.org/10.3390/mi14020334