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The creation of compact thermal models of electronic components using model reduction
32
Citations
22
References
2005
Year
EngineeringEnergy EfficiencyPower ElectronicsEngineering ThermodynamicsRefrigerationNumerical SimulationThermal AnalysisModeling And SimulationThermal ModelingThermodynamicsElectronic PackagingThermal ConductionDevice ModelingIc ComponentElectrical EngineeringThermal ModelComputer EngineeringHeat TransferMicroelectronicsCompact Thermal ModelsThermal ManagementElectronic ComponentsModel ReductionThermal EngineeringCircuit SimulationMultiscale Modeling
This paper presents a new approach to compact thermal modeling. The paper shows how a parameterized reduced thermal model of an IC component can be created based on a parametric model reduction technique. By applying this technique, a large system of equations characterizing a discretized fully detailed numerical thermal model can be drastically reduced. The final product of a parameterized model reduction procedure is a set of small matrices presenting an abstract description of the component thermal behavior. The reduced system can be used to either synthesize a resistive network or formulate a set of connection equations to be connected to higher simulation levels. External boundary conditions are parameters of the reduced model and can be specified at simulation time. A parameterized reduced thermal model is found to have a number of advantages over an optimized resistor network model. The model can be generated quickly (one lower-upper (LU) decomposition is needed), high accuracies are obtained with a typical error of less than 0.1%. The technique also predicts temperature at all internal nodes of the original detailed model not just a single junction temperature. In this paper, the new technique is demonstrated through two examples of realistic IC components: a GaAs power amplifier and a generic multichip module ball grid array package. Both reduced models are connected to substrates in a number of different configurations. Thermal analysis performed in each case shows the importance of the geometric configuration of the connections on predictive capability.
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