Thermal design and characterization of a modular integrated liquid cooled 1200 V-35 A SiC MOSFET bi-directional switch

Cova, Paolo; Aliyu, Attahir Murtala; Castellazzi, Alberto; Chiozzi, Diego; Delmonte, Nicola; Lasserre, Philippe; Pignoloni, N.

doi:10.1016/j.microrel.2017.06.062

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…No sign of degradation was apparent after 500 cycles. Thermal simulation and FLIR camera measurement showed that with 100 W dissipated in each MOSFET, junction temperature could be kept below 80°C [44]. The main elements of this design are shown in Figure 9.…”

Section: Examples Of Advanced Packaging Concept Implementationmentioning

confidence: 99%

High-Performance Packaging Technology for Wide Bandgap Semiconductor Modules

Mumby-Croft¹,

Li²,

Dai³

et al. 2018

Disruptive Wide Bandgap Semiconductors, Related Technologies, and Their Applications

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The properties of wide band gap (WBG) semiconductors are beneficial to power electronics applications ranging from consumer electronics and renewable energy to electric vehicles and high-power traction applications like high-speed trains. WBG devices, properly integrated, will allow power electronics systems to be smaller, lighter, operate at higher temperatures, and at higher frequencies than previous generations of Si-based systems. These will contribute to higher efficiency, and therefore, lower lifecycle costs and lower CO 2 emissions. Over 20 years have been spent developing WBG materials, low-defect-density wafers, epitaxy, and device fabrication and processing technology. In power electronics applications, devices are normally packaged into large integrated modules with electrical, mechanical and thermal connection to the system and control circuit. The first generations of WBG device have used conventional or existing module designs to allow drop-in replacement of Si devices; this approach limits the potential benefit. To realize the full potential of WBG devices, especially the higher operating temperatures and faster switching frequency, a new generation of packaging design and technology concepts must be widely implemented.

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Section: Examples Of Advanced Packaging Concept Implementationmentioning

confidence: 99%

High-Performance Packaging Technology for Wide Bandgap Semiconductor Modules

Mumby-Croft¹,

Li²,

Dai³

et al. 2018

Disruptive Wide Bandgap Semiconductors, Related Technologies, and Their Applications

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“…It is challenging to obtain analytical solutions to the three-dimensional Poisson's equation to arrive at the temperature map for a complex commercial package. Therefore, numerous studies have analyzed the influence of cooling technologies and package designs on the thermal performance using either a thermal resistance network analogy (Foster or Cauer network) or via 3D simulations [9][10][11]. Thermal resistance circuits can capture heat spreading within different layers, and can be used to model slightly complicated geometries without resorting to full-scale threedimensional (3D) simulations.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Predictions of Benefits of High Thermal Conductivity Encapsulation Materials for Power Electronics Packaging

Acharya

Lokanathan

Ouroua

et al. 2021

Journal of Electronic Packaging

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Machine learning (ML)-based predictive techniques are used in conjunction with a game-theoretic approach to predict the thermal behavior of a power electronics package and study the relative influence of encapsulation material properties and thermal management in influencing hotspot temperatures. Parametric steady-state and transient thermal simulations are conducted for a commercially available 1.2 kV/444 A SiC half-bridge module. An extensive databank of 2592 (steady-state) and 1200 (transient) data points generated via numerical simulations is used to train and evaluate the performance of three ML algorithms (random forest, support vector machine and neural network) in modeling the thermal behavior. The parameter space includes the thermal conductivities of the encapsulant, baseplate, heat sink and cooling conditions deployed at the sink; the parametric space covers a variety of materials and cooling scenarios. Excellent prediction accuracies with R2 values > 99.5% are obtained for the algorithms. SHAP (Shapley Additive exPlanations) dependence plots are used to quantify the relative impact of device and heat sink parameters on junction temperatures. We observe that while heatsink cooling conditions significantly influence the steady-state junction temperature, their contribution in determining the junction temperature in dynamic mode is diminished. Using ML-SHAP models, we quantify the impact of emerging polymeric nanocomposites (with high conductivities and diffusivities) on hotspot temperature reduction, with the device operating in static and dynamic modes. Overall, this study highlights the attractiveness of ML-based approaches for thermal design, and provides a framework for setting targets for future encapsulation materials.

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