Predicting Lifetime of Semiconductor Power Devices Under Power Cycling Stress Using Artificial Neural Network

Vaccaro, Alessandro; Magnone, Paolo; Zilio, Andrea; Mattavelli, Paolo

doi:10.1109/jestpe.2022.3194189

Cited by 7 publications

(6 citation statements)

References 41 publications

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“…Picture of the setup. The test circuit is placed on a liquid-cooled thermal plate, whose temperature is fixed by means of a temperature controller [17]. Fig.…”

Section: Active Control Of δTjmentioning

confidence: 99%

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Influence of Power Cycling Test Methodology on the Applicability of the Linear Damage Accumulation Rule for the Lifetime Estimation in Power Devices

Vaccaro

Magnone

2023

IEEE Trans. Power Electron.

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The lifetime of power semiconductor devices, operating under a given mission profile and subjected to power cycling stress, is conventionally estimated under the assumption of linear damage accumulation rule, that is the application of the Miner's rule. To this purpose, lifetime models must be properly defined allowing to take into account for the relevant parameters of power cycling stress. This work shows how to estimate a cumulative distribution function in the case of an arbitrary temperature swing profile, starting from the statistical distribution at constant power cycling conditions. It is found that the accuracy of the linear damage accumulation rule is related to the experimental methodology adopted for power cycling tests. A detailed experimental activity is carried out on packaged IGBT devices, providing useful guidelines for the definition of lifetime models to be adopted in the Miner's rule.

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“…Picture of the setup. The test circuit is placed on a liquid-cooled thermal plate, whose temperature is fixed by means of a temperature controller [17]. Fig.…”

Section: Active Control Of δTjmentioning

confidence: 99%

“…The above-mentioned degradation mechanisms are mainly triggered by the temperature cycling, but they are also affected by the average temperature and the heating time. Several models have been reported in literature, allowing to account for these parameters [11]- [17].…”

Section: Introductionmentioning

confidence: 99%

Influence of Power Cycling Test Methodology on the Applicability of the Linear Damage Accumulation Rule for the Lifetime Estimation in Power Devices

Vaccaro

Magnone

2023

IEEE Trans. Power Electron.

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“…In general, the lifetime of power components can be estimated by considering model-driven and data-driven approaches. Model-driven approach can be either empirical [9]- [15] (i.e. calibrated according to accelerated lifetime tests), or physicsbased [16], [17].…”

Section: Manymentioning

confidence: 99%

Remaining Useful Lifetime Prediction of Discrete Power Devices by Means of Artificial Neural Networks

Vaccaro,

Biadene,

Magnone

2023

IEEE Open J. Power Electron.

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This work proposes a deep learning-based model for predicting the lifetime of power devices subjected to power cycling. To this purpose, a neural network based on bidirectional long short-term memory is adopted. The neural network is trained with experimental on-voltage degradation profiles. The application of the proposed method is based on the monitoring of a precursor, that is the on-voltage degradation. According to considered precursor, the model allows predicting the remaining useful lifetime (RUL) of power components. In order to prove the accuracy of the model, TO-247 power devices are stressed under power cycling and their wear-out is experimentally investigated. RUL predicted by the neural network is then compared with the experimental lifetime of power devices. Thanks to the proposed deep learning model, the accuracy in the lifetime estimation improves as long as more information about the state of health of the device under test is acquired.

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“…In this work, we aim to propose a machine learning (ML) model, which enables to predict the microstructural items, which are the indicators of IMC-growth mechanism under thermal cycling. In the microelectronics industry, the ML-based model has been extensively used to predict the reliability of components on the basis of physical and mechanical parameters without considering the microstructural characteristics [22][23][24][25][26]. Hence, our ML model can open a new solution for evaluating the solder microstructure in a wide range of thermal cycles by just examining few samples.…”

Section: Introductionmentioning

confidence: 99%

Expectation–maximization machine learning model for micromechanical evaluation of thermally-cycled solder joints in a semiconductor

Chen

2023

J. Phys.: Condens. Matter

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This paper aims to study the microstructural and micromechanical variations of solder joints in a semiconductor under the evolution of thermal-cycling loading. For this purpose, a model was developed on the basis of expectation-maximization machine learning (ML) and nanoindentation mapping. Using this model, it is possible to predict and interpret the microstructural features of solder joints through the micromechanical variations (i.e. elastic modulus) of interconnection. According to the results, the classification of Sn-based matrix, intermetallic compounds and the grain boundaries with specified elastic-modulus ranges was successfully performed through the ML model. However, it was detected some overestimations in regression process when the interfacial regions got thickened in the microstructure. The ML outcomes also revealed that the thermal-cycling evolution was accompanied with stiffening and growth of intermetallic compounds; while the spatial portion of Sn-based matrix decreased in the microstructure. It was also figured out that the stiffness gradient becomes intensified in the treated samples, which is consistent with this fact that the thermal cycling increases the mechanical mismatch between the matrix and the intermetallic compounds.

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Predicting Lifetime of Semiconductor Power Devices Under Power Cycling Stress Using Artificial Neural Network

Cited by 7 publications

References 41 publications

Influence of Power Cycling Test Methodology on the Applicability of the Linear Damage Accumulation Rule for the Lifetime Estimation in Power Devices

Influence of Power Cycling Test Methodology on the Applicability of the Linear Damage Accumulation Rule for the Lifetime Estimation in Power Devices

Remaining Useful Lifetime Prediction of Discrete Power Devices by Means of Artificial Neural Networks

Expectation–maximization machine learning model for micromechanical evaluation of thermally-cycled solder joints in a semiconductor

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