IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2022 · 14 citations · 20 references
ComputingComputational EngineeringComputer ScienceComputational Heat TransferMachine LearningPhotonicsDevice ModelingHeat Transfer AnalysisHeat Transfer ModelingPhotonic Device SimulationThermal DesignThermal SimulationFinite Element Thermal AnalysisIntegrated Photonic Circuit SimulationMultiscale Thermal ModelingPerformance BenchmarkingProper Orthogonal DecompositionTemperature SimulationThermal Device SimulationTraining Time
Multiscale thermal analysis in integrated systems is required for capturing both device-level and circuit-level dynamics. Traditional analysis with finite element (FE) models can be accelerated by using machine learning (ML) methods. In this article, a performance benchmarking between three ML methods for thermal simulation is carried out: 1) artificial neural networks (ANNs); 2) proper orthogonal decomposition with radial basis functions (POD-RBFs); and 3) finally, POD-RBF-ANN is used as a hybrid ML method. The (dis)advantages of the different methods are demonstrated for the thermal simulation of a multiscale photonic chip. The ML models are trained with FE data for both linear and nonlinear dynamics and are tested for inter- and extra-polation prediction accuracy. A computational speed increase with a factor >7500 compared to FE is obtained. Furthermore, ANNs prove to be the best suited for the simulation of nonlinear dynamics. POD-RBF is the best method for minimizing training time and combining the best of both methods in POD-RBF-ANN creates an ML model with a short training phase and highly accurate predictions.
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