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Erschienen in: Journal of Electronic Materials 2/2024

21.12.2023 | Original Research Article

A Probabilistic Bayesian Machine Learning Framework for Comprehensive Characterization of Bond Wires in IGBT Modules Under Thermomechanical Loadings

verfasst von: Max-Fredi Quispe-Aguilar, Rosa Huaraca Aparco, Calixto Cañari Otero, Margoth Moreno Huamán, Yersi-Luis Huamán-Romaní

Erschienen in: Journal of Electronic Materials | Ausgabe 2/2024

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Abstract

A Bayesian machine learning (ML) framework was introduced for the comprehensive characterization of bond wires within insulated gate bipolar transistor (IGBT) modules under the influence of thermomechanical loadings. The primary objective of this work was to predict two critical performance metrics, namely, equivalent plastic deformation (EPD) and the number of cycles to failure (Nf). At the core of our investigation was the dependable acquisition of training data via finite element method simulations. Based on the results, exceptional predictive accuracy was achieved, as evidenced by the impressive R-squared values of 0.962 for EPD and 0.927 for Nf, both of which are obtained from the Bayesian ML model. The high performance of the Bayesian model can be attributed to its ability to effectively capture complex relationships within the data while simultaneously being robust in handling uncertainties, rendering it suitable for situations characterized by limited datasets. Furthermore, it was revealed that the weight functions of the input parameters were significantly influenced by the values of the output targets, illustrating the distinct dependencies between each output target (EPD and Nf) and the relevant input features. These findings contribute to a deeper comprehension of the intricate interactions between input parameters and output metrics, ultimately aiding in the development of more precise and dependable models for bond wire characterization in IGBT modules.

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Literatur
1.
Zurück zum Zitat X.L. Wu, C.Y. Li, J.L. Yang, Y. Liu, and X.H. Han, Theoretical and experimental research on flow boiling heat transfer in microchannels for IGBT modules. Int. J. Heat Mass Transf. 205, 123900 (2023).CrossRef X.L. Wu, C.Y. Li, J.L. Yang, Y. Liu, and X.H. Han, Theoretical and experimental research on flow boiling heat transfer in microchannels for IGBT modules. Int. J. Heat Mass Transf. 205, 123900 (2023).CrossRef
2.
Zurück zum Zitat S. Gao, R. Wang, H. Wang, and R. Kang, Warping model of high-power IGBT modules subjected to reflow soldering process. Int. J. Mech. Sci. 251, 108350 (2023).CrossRef S. Gao, R. Wang, H. Wang, and R. Kang, Warping model of high-power IGBT modules subjected to reflow soldering process. Int. J. Mech. Sci. 251, 108350 (2023).CrossRef
3.
Zurück zum Zitat B. Zhou, F. Zeng, X. Jiang, W. Lian, B. Shi, and P. Zhang, Thermal properties of low-temperature-sintered graphene/nano-silver paste for insulated gate bipolar transistor packages. J. Electron. Mater. 52, 4979 (2023).CrossRef B. Zhou, F. Zeng, X. Jiang, W. Lian, B. Shi, and P. Zhang, Thermal properties of low-temperature-sintered graphene/nano-silver paste for insulated gate bipolar transistor packages. J. Electron. Mater. 52, 4979 (2023).CrossRef
4.
Zurück zum Zitat L. Karanja, P. Pichon, J. Brandelero, and M. Legros, Effect of post bonding annealing on the reliability of Al based wire bondings in IGBTs. Microelectron. Reliab. 138, 114647 (2022).CrossRef L. Karanja, P. Pichon, J. Brandelero, and M. Legros, Effect of post bonding annealing on the reliability of Al based wire bondings in IGBTs. Microelectron. Reliab. 138, 114647 (2022).CrossRef
5.
Zurück zum Zitat H. Zhou, Y. Zhang, J. Cao, C. Su, C. Li, A. Chang, and B. An, Research progress on bonding wire for microelectronic packaging. Micromachines 14, 432 (2023).CrossRef H. Zhou, Y. Zhang, J. Cao, C. Su, C. Li, A. Chang, and B. An, Research progress on bonding wire for microelectronic packaging. Micromachines 14, 432 (2023).CrossRef
6.
Zurück zum Zitat A. Abuelnaga, M. Narimani, and A.S. Bahman, A review on IGBT module failure modes and lifetime testing. IEEE Access 9, 9643 (2021).CrossRef A. Abuelnaga, M. Narimani, and A.S. Bahman, A review on IGBT module failure modes and lifetime testing. IEEE Access 9, 9643 (2021).CrossRef
8.
Zurück zum Zitat L. Xie, E. Deng, S. Yang, Y. Zhang, Y. Zhong, Y. Wang, and Y. Huang, State-of-the-art of the bond wire failure mechanism and power cycling lifetime in power electronics. Microelectron. Reliab. 147, 115060 (2023).CrossRef L. Xie, E. Deng, S. Yang, Y. Zhang, Y. Zhong, Y. Wang, and Y. Huang, State-of-the-art of the bond wire failure mechanism and power cycling lifetime in power electronics. Microelectron. Reliab. 147, 115060 (2023).CrossRef
9.
Zurück zum Zitat N. Dornic, A. Ibrahim, Z. Khatir, N. Degrenne, S. Mollov, and D. Ingrosso, Analysis of the aging mechanism occurring at the bond-wire contact of IGBT power devices during power cycling. Microelectron. Reliab. 114, 113873 (2020).CrossRef N. Dornic, A. Ibrahim, Z. Khatir, N. Degrenne, S. Mollov, and D. Ingrosso, Analysis of the aging mechanism occurring at the bond-wire contact of IGBT power devices during power cycling. Microelectron. Reliab. 114, 113873 (2020).CrossRef
10.
Zurück zum Zitat F. Qin, X. Bie, T. An, J. Dai, Y. Dai, and P. Chen, A lifetime prediction method for IGBT modules considering the self-accelerating effect of bond wire damage. IEEE J. Emerg. Sel. Top. Power Electron. 9, 2271 (2021).CrossRef F. Qin, X. Bie, T. An, J. Dai, Y. Dai, and P. Chen, A lifetime prediction method for IGBT modules considering the self-accelerating effect of bond wire damage. IEEE J. Emerg. Sel. Top. Power Electron. 9, 2271 (2021).CrossRef
11.
Zurück zum Zitat Y. Huang, Y. Jia, Y. Luo, F. Xiao, and B. Liu, Lifting-off of al bonding wires in IGBT modules under power cycling: failure mechanism and lifetime model. IEEE J. Emerg. Sel. Top. Power Electron. 8, 3162 (2020).CrossRef Y. Huang, Y. Jia, Y. Luo, F. Xiao, and B. Liu, Lifting-off of al bonding wires in IGBT modules under power cycling: failure mechanism and lifetime model. IEEE J. Emerg. Sel. Top. Power Electron. 8, 3162 (2020).CrossRef
12.
Zurück zum Zitat S. Manoharan, C. Patel, S. Dunford, J. Beshears, and P. McCluskey, Life prediction of copper wire bonds in commercial devices using principal component analysis (PCA). Microelectron. Reliab. 99, 137 (2019).CrossRef S. Manoharan, C. Patel, S. Dunford, J. Beshears, and P. McCluskey, Life prediction of copper wire bonds in commercial devices using principal component analysis (PCA). Microelectron. Reliab. 99, 137 (2019).CrossRef
13.
Zurück zum Zitat Y. Zhang, K. Wu, H. Li, S. Shen, W. Cao, F. Li, and J. Han, Thermal fatigue analysis of gold wire bonding solder joints in MEMS pressure sensors by thermal cycling tests. Microelectron. Reliab. 139, 114829 (2022).CrossRef Y. Zhang, K. Wu, H. Li, S. Shen, W. Cao, F. Li, and J. Han, Thermal fatigue analysis of gold wire bonding solder joints in MEMS pressure sensors by thermal cycling tests. Microelectron. Reliab. 139, 114829 (2022).CrossRef
15.
Zurück zum Zitat Y. Huang, Y. Luo, F. Xiao, B. Liu, and X. Tang, Evaluation of the degradation in electrothermal characteristics of IGBTs during thermal cycling cocaused by solder cracking and Al-wires lifting-off based on iterative looping. IEEE Trans. Power Electron. 38, 1768 (2023).CrossRef Y. Huang, Y. Luo, F. Xiao, B. Liu, and X. Tang, Evaluation of the degradation in electrothermal characteristics of IGBTs during thermal cycling cocaused by solder cracking and Al-wires lifting-off based on iterative looping. IEEE Trans. Power Electron. 38, 1768 (2023).CrossRef
17.
Zurück zum Zitat Q. Huang, C. Peng, S.F.-M. Ellen, W. Zhu, and L. Wang, A finite element analysis on the reliability of heavy bonding wire for high-power IGBT module. IEEE Trans. Compon. Packag. Manuf. Technol. 11, 212 (2021).CrossRef Q. Huang, C. Peng, S.F.-M. Ellen, W. Zhu, and L. Wang, A finite element analysis on the reliability of heavy bonding wire for high-power IGBT module. IEEE Trans. Compon. Packag. Manuf. Technol. 11, 212 (2021).CrossRef
18.
Zurück zum Zitat L. Li, Y. He, L. Wang, C. Wang, and X. Liu, IGBT lifetime model considering composite failure modes. Mater. Sci. Semicond. Process. 143, 106529 (2022).CrossRef L. Li, Y. He, L. Wang, C. Wang, and X. Liu, IGBT lifetime model considering composite failure modes. Mater. Sci. Semicond. Process. 143, 106529 (2022).CrossRef
20.
Zurück zum Zitat V. Samavatian, M. Fotuhi-Firuzabad, M. Samavatian, P. Dehghanian, and F. Blaabjerg, Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics. Sci. Rep. 10, 14821 (2020).CrossRef V. Samavatian, M. Fotuhi-Firuzabad, M. Samavatian, P. Dehghanian, and F. Blaabjerg, Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics. Sci. Rep. 10, 14821 (2020).CrossRef
21.
Zurück zum Zitat V. Voet, F. Van Loock, C. De Fruytier, A. Simar, and T. Pardoen, Machine learning aided modelling of thermomechanical fatigue of solder joints in electronic component assemblies. Int. J. Fatigue 167, 107298 (2023).CrossRef V. Voet, F. Van Loock, C. De Fruytier, A. Simar, and T. Pardoen, Machine learning aided modelling of thermomechanical fatigue of solder joints in electronic component assemblies. Int. J. Fatigue 167, 107298 (2023).CrossRef
22.
Zurück zum Zitat R.R. Kurniawan, B.T. Sayed, A. Sari, J.P. Luna, A.K. Kareem, and N.A. Hussien, A micromechanical data-driven machine-learning approach for microstructural characterization of solder balls in electronic packages subjected to thermomechanical fatigue. J. Electron. Mater. 52, 4614 (2023).CrossRef R.R. Kurniawan, B.T. Sayed, A. Sari, J.P. Luna, A.K. Kareem, and N.A. Hussien, A micromechanical data-driven machine-learning approach for microstructural characterization of solder balls in electronic packages subjected to thermomechanical fatigue. J. Electron. Mater. 52, 4614 (2023).CrossRef
23.
Zurück zum Zitat V. Samavatian, M. Fotuhi-Firuzabad, M. Samavatian, P. Dehghanian, and F. Blaabjerg, Iterative machine learning-aided framework bridges between fatigue and creep damages in solder interconnections. IEEE Trans. Compon. Packag. Manuf. Technol. 12, 349 (2022).CrossRef V. Samavatian, M. Fotuhi-Firuzabad, M. Samavatian, P. Dehghanian, and F. Blaabjerg, Iterative machine learning-aided framework bridges between fatigue and creep damages in solder interconnections. IEEE Trans. Compon. Packag. Manuf. Technol. 12, 349 (2022).CrossRef
24.
Zurück zum Zitat B. Zhang and Y. Gao, IGBT reliability analysis of photovoltaic inverter with reactive power output capability. Microelectron. Reliab. 147, 115073 (2023).CrossRef B. Zhang and Y. Gao, IGBT reliability analysis of photovoltaic inverter with reactive power output capability. Microelectron. Reliab. 147, 115073 (2023).CrossRef
25.
Zurück zum Zitat T.-C. Chen, M.J.C. Opulencia, H.S. Majdi, A.T. Hammid, H. Sharma, S. Sajjadifar, and A. Surendar, Estimation of thermomechanical fatigue lifetime of ball grid solder joints in electronic devices using a machine learning approach. J. Electron. Mater. 51, 3495 (2022).CrossRef T.-C. Chen, M.J.C. Opulencia, H.S. Majdi, A.T. Hammid, H. Sharma, S. Sajjadifar, and A. Surendar, Estimation of thermomechanical fatigue lifetime of ball grid solder joints in electronic devices using a machine learning approach. J. Electron. Mater. 51, 3495 (2022).CrossRef
26.
Zurück zum Zitat X. Dai, X. Yang, X. Wu, C. Tu, and G. Liu, Analytical modeling of thermomechanical stress for bond wire of IGBT module. Microelectron. Reliab. 127, 114401 (2021).CrossRef X. Dai, X. Yang, X. Wu, C. Tu, and G. Liu, Analytical modeling of thermomechanical stress for bond wire of IGBT module. Microelectron. Reliab. 127, 114401 (2021).CrossRef
27.
Zurück zum Zitat N. Dornic, Z. Khatir, S.H. Tran, A. Ibrahim, R. Lallemand, J.-P. Ousten, J. Ewanchuk, and S.V. Mollov, Stress-based model for lifetime estimation of bond wire contacts using power cycling tests and finite-element modeling. IEEE J. Emerg. Sel. Top. Power Electron. 7, 1659 (2019).CrossRef N. Dornic, Z. Khatir, S.H. Tran, A. Ibrahim, R. Lallemand, J.-P. Ousten, J. Ewanchuk, and S.V. Mollov, Stress-based model for lifetime estimation of bond wire contacts using power cycling tests and finite-element modeling. IEEE J. Emerg. Sel. Top. Power Electron. 7, 1659 (2019).CrossRef
28.
Zurück zum Zitat M. Hernes, S. D’Arco, A. Antonopoulos, and D. Peftitsis, Failure analysis and lifetime assessment of IGBT power modules at low temperature stress cycles. IET Power Electron. 14, 1271 (2021).CrossRef M. Hernes, S. D’Arco, A. Antonopoulos, and D. Peftitsis, Failure analysis and lifetime assessment of IGBT power modules at low temperature stress cycles. IET Power Electron. 14, 1271 (2021).CrossRef
29.
Zurück zum Zitat P.A. Agyakwa, L. Yang, E. Arjmand, P. Evans, M.R. Corfield, and C.M. Johnson, Damage evolution in Al wire bonds subjected to a junction temperature fluctuation of 30 K. J. Electron. Mater. 45, 3659 (2016).CrossRef P.A. Agyakwa, L. Yang, E. Arjmand, P. Evans, M.R. Corfield, and C.M. Johnson, Damage evolution in Al wire bonds subjected to a junction temperature fluctuation of 30 K. J. Electron. Mater. 45, 3659 (2016).CrossRef
30.
Zurück zum Zitat W. Huai, M. Liserre, F. Blaabjerg, P. De Place Rimmen, J.B. Jacobsen, T. Kvisgaard, and J. Landkildehus, Transitioning to physics-of-failure as a reliability driver in power electronics. IEEE J. Emerg. Sel. Top. Power Electron. 2, 97 (2014).CrossRef W. Huai, M. Liserre, F. Blaabjerg, P. De Place Rimmen, J.B. Jacobsen, T. Kvisgaard, and J. Landkildehus, Transitioning to physics-of-failure as a reliability driver in power electronics. IEEE J. Emerg. Sel. Top. Power Electron. 2, 97 (2014).CrossRef
31.
Zurück zum Zitat V. Samavatian, Y. Avenas, and H. Iman-Eini, Mutual and self-aging effects of power semiconductors on the thermal behaviour of DC–DC boost power converter. Microelectron. Reliab. 88–90, 493 (2018).CrossRef V. Samavatian, Y. Avenas, and H. Iman-Eini, Mutual and self-aging effects of power semiconductors on the thermal behaviour of DC–DC boost power converter. Microelectron. Reliab. 88–90, 493 (2018).CrossRef
32.
Zurück zum Zitat M. Bouarroudj, Z. Khatir, J.P. Ousten, F. Badel, L. Dupont, and S. Lefebvre, Degradation behavior of 600 V-200 A IGBT modules under power cycling and high temperature environment conditions. Microelectron. Reliab. 47, 1719 (2007).CrossRef M. Bouarroudj, Z. Khatir, J.P. Ousten, F. Badel, L. Dupont, and S. Lefebvre, Degradation behavior of 600 V-200 A IGBT modules under power cycling and high temperature environment conditions. Microelectron. Reliab. 47, 1719 (2007).CrossRef
33.
Zurück zum Zitat S. Dusmez, S.H. Ali, M. Heydarzadeh, A.S. Kamath, H. Duran, and B. Akin, Aging precursor identification and lifetime estimation for thermally aged discrete package silicon power switches. IEEE Trans. Ind. Appl. 53, 251 (2017).CrossRef S. Dusmez, S.H. Ali, M. Heydarzadeh, A.S. Kamath, H. Duran, and B. Akin, Aging precursor identification and lifetime estimation for thermally aged discrete package silicon power switches. IEEE Trans. Ind. Appl. 53, 251 (2017).CrossRef
34.
Zurück zum Zitat A.K. Pani and H.K. Mohanta, Online monitoring of cement clinker quality using multivariate statistics and Takagi-Sugeno fuzzy-inference technique. Control Eng. Pract. 57, 1 (2016).CrossRef A.K. Pani and H.K. Mohanta, Online monitoring of cement clinker quality using multivariate statistics and Takagi-Sugeno fuzzy-inference technique. Control Eng. Pract. 57, 1 (2016).CrossRef
35.
Zurück zum Zitat G. Li, L. Yang, C.-G. Lee, X. Wang, and M. Rong, A Bayesian deep learning RUL framework integrating epistemic and aleatoric uncertainties. IEEE Trans. Ind. Electron. 68, 8829 (2020).CrossRef G. Li, L. Yang, C.-G. Lee, X. Wang, and M. Rong, A Bayesian deep learning RUL framework integrating epistemic and aleatoric uncertainties. IEEE Trans. Ind. Electron. 68, 8829 (2020).CrossRef
36.
Zurück zum Zitat H. Pei, X.-S. Si, C. Hu, T. Li, C. He, and Z. Pang, Bayesian deep-learning-based prognostic model for equipment without label data related to lifetime. IEEE Trans. Syst. Man, Cybern. Syst. 53, 504 (2022).CrossRef H. Pei, X.-S. Si, C. Hu, T. Li, C. He, and Z. Pang, Bayesian deep-learning-based prognostic model for equipment without label data related to lifetime. IEEE Trans. Syst. Man, Cybern. Syst. 53, 504 (2022).CrossRef
37.
Zurück zum Zitat X. Ke and Y. Duan, A Bayesian machine learning approach for inverse prediction of high-performance concrete ingredients with targeted performance. Constr. Build. Mater. 270, 121424 (2021).CrossRef X. Ke and Y. Duan, A Bayesian machine learning approach for inverse prediction of high-performance concrete ingredients with targeted performance. Constr. Build. Mater. 270, 121424 (2021).CrossRef
39.
Zurück zum Zitat S. Roberts, M. Osborne, M. Ebden, S. Reece, N. Gibson, and S. Aigrain, Gaussian processes for time-series modelling. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 371, 20110550 (2013).CrossRef S. Roberts, M. Osborne, M. Ebden, S. Reece, N. Gibson, and S. Aigrain, Gaussian processes for time-series modelling. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 371, 20110550 (2013).CrossRef
40.
Zurück zum Zitat F. Gao, and L. Han, Implementing the Nelder-Mead simplex algorithm with adaptive parameters. Comput. Optim. Appl. 51, 259 (2012).CrossRef F. Gao, and L. Han, Implementing the Nelder-Mead simplex algorithm with adaptive parameters. Comput. Optim. Appl. 51, 259 (2012).CrossRef
41.
Zurück zum Zitat Z. Xiong, Y. Cui, Z. Liu, Y. Zhao, M. Hu, and J. Hu, Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation. Comput. Mater. Sci. 171, 109203 (2020).CrossRef Z. Xiong, Y. Cui, Z. Liu, Y. Zhao, M. Hu, and J. Hu, Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation. Comput. Mater. Sci. 171, 109203 (2020).CrossRef
42.
Zurück zum Zitat G.S. Thoppil, J. Nie, and A. Alankar, Bayesian approach for inferrable machine learning models of process–structure–property linkages in complex concentrated alloys. J. Alloys Compd. 967, 171595 (2023).CrossRef G.S. Thoppil, J. Nie, and A. Alankar, Bayesian approach for inferrable machine learning models of process–structure–property linkages in complex concentrated alloys. J. Alloys Compd. 967, 171595 (2023).CrossRef
43.
Zurück zum Zitat V.A. Mints, J.K. Pedersen, A. Bagger, J. Quinson, A.S. Anker, K.M.Ø. Jensen, J. Rossmeisl, and M. Arenz, Exploring the composition space of high-entropy alloy nanoparticles for the electrocatalytic H2/CO oxidation with bayesian optimization. ACS Catal. 12, 11263 (2022).CrossRef V.A. Mints, J.K. Pedersen, A. Bagger, J. Quinson, A.S. Anker, K.M.Ø. Jensen, J. Rossmeisl, and M. Arenz, Exploring the composition space of high-entropy alloy nanoparticles for the electrocatalytic H2/CO oxidation with bayesian optimization. ACS Catal. 12, 11263 (2022).CrossRef
44.
Zurück zum Zitat I. Jebli, F.-Z. Belouadha, M.I. Kabbaj, and A. Tilioua, Prediction of solar energy guided by pearson correlation using machine learning. Energy 224, 120109 (2021).CrossRef I. Jebli, F.-Z. Belouadha, M.I. Kabbaj, and A. Tilioua, Prediction of solar energy guided by pearson correlation using machine learning. Energy 224, 120109 (2021).CrossRef
45.
Zurück zum Zitat B. Czerny, I. Paul, G. Khatibi, and M. Thoben, Experimental and analytical study of geometry effects on the fatigue life of Al bond wire interconnects. Microelectron. Reliab. 53, 1558 (2013).CrossRef B. Czerny, I. Paul, G. Khatibi, and M. Thoben, Experimental and analytical study of geometry effects on the fatigue life of Al bond wire interconnects. Microelectron. Reliab. 53, 1558 (2013).CrossRef
46.
Zurück zum Zitat M.A. Eleffendi and C.M. Johnson, In-service diagnostics for wire-bond lift-off and solder fatigue of power semiconductor packages. IEEE Trans. Power Electron. 32, 7187 (2017).CrossRef M.A. Eleffendi and C.M. Johnson, In-service diagnostics for wire-bond lift-off and solder fatigue of power semiconductor packages. IEEE Trans. Power Electron. 32, 7187 (2017).CrossRef
47.
Zurück zum Zitat H. Luo, F. Iannuzzo, N. Baker, F. Blaabjerg, W. Li, and X. He, Study of current density influence on bond wire degradation rate in SiC MOSFET modules. IEEE J. Emerg. Sel. Top. Power Electron. 8, 1622 (2019).CrossRef H. Luo, F. Iannuzzo, N. Baker, F. Blaabjerg, W. Li, and X. He, Study of current density influence on bond wire degradation rate in SiC MOSFET modules. IEEE J. Emerg. Sel. Top. Power Electron. 8, 1622 (2019).CrossRef
48.
Zurück zum Zitat S. Palanisamy, T. Basler, J. Lutz, C. Künzel, L. Wehrhahn-Kilian, and R. Elpelt, Investigation of the bipolar degradation of SiC MOSFET body diodes and the influence of current density, (2021), pp. 1–6. S. Palanisamy, T. Basler, J. Lutz, C. Künzel, L. Wehrhahn-Kilian, and R. Elpelt, Investigation of the bipolar degradation of SiC MOSFET body diodes and the influence of current density, (2021), pp. 1–6.
49.
Zurück zum Zitat U.-M. Choi, F. Blaabjerg, and S. Jørgensen, Power cycling test methods for reliability assessment of power device modules in respect to temperature stress. IEEE Trans. Power Electron. 33, 2531 (2017).CrossRef U.-M. Choi, F. Blaabjerg, and S. Jørgensen, Power cycling test methods for reliability assessment of power device modules in respect to temperature stress. IEEE Trans. Power Electron. 33, 2531 (2017).CrossRef
50.
Zurück zum Zitat F. Erturk, E. Ugur, J. Olson, and B. Akin, Real-time aging detection of SiC MOSFETs. IEEE Trans. Ind. Appl. 55, 600 (2018).CrossRef F. Erturk, E. Ugur, J. Olson, and B. Akin, Real-time aging detection of SiC MOSFETs. IEEE Trans. Ind. Appl. 55, 600 (2018).CrossRef
Metadaten
Titel
A Probabilistic Bayesian Machine Learning Framework for Comprehensive Characterization of Bond Wires in IGBT Modules Under Thermomechanical Loadings
verfasst von
Max-Fredi Quispe-Aguilar
Rosa Huaraca Aparco
Calixto Cañari Otero
Margoth Moreno Huamán
Yersi-Luis Huamán-Romaní
Publikationsdatum
21.12.2023
Verlag
Springer US
Erschienen in
Journal of Electronic Materials / Ausgabe 2/2024
Print ISSN: 0361-5235
Elektronische ISSN: 1543-186X
DOI
https://doi.org/10.1007/s11664-023-10868-y

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