Fast Thermal Simulation of FinFET Circuits Based on a Multiblock Reduced-Order Model

Jia, Wangkun; Helenbrook, Brian T.; Cheng, Ming‐C.

doi:10.1109/tcad.2015.2501305

Cited by 22 publications

(6 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They can be obtained by finding a function Ψ which maximizes the inner product with the temperature field in the training data. The function which accomplishes this, will be the POD modes φ [30], [31]:…”

Section: B Pod-rbfmentioning

confidence: 99%

Benchmarking of Machine Learning Methods for Multiscale Thermal Simulation of Integrated Circuits

Coenen

Oprins

Degraeve

et al. 2023

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

View full text Add to dashboard Cite

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 paper a performance benchmarking between three ML methods for thermal simulation is carried out: Artificial Neural Networks (ANNs), Proper Orthogonal Decomposition with Radial Basis Functions (POD-RBF) and 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 non-linear dynamics and are tested for inter-and extrapolation prediction accuracy. A computational speed increase with factor >7500 compared to FE is obtained. Furthermore, ANNs prove to be the best suited for the simulation of non-linear dynamics. POD-RBF is the best method for minimizing training time and combining the best of both methods in POD-RBF-ANN creates a ML model with short training phase and highly accurate predictions.

show abstract

Section: B Pod-rbfmentioning

confidence: 99%

Benchmarking of Machine Learning Methods for Multiscale Thermal Simulation of Integrated Circuits

Coenen

Oprins

Degraeve

et al. 2023

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

View full text Add to dashboard Cite

show abstract

“…Virtual prototyping of power electronic systems is discussed in [28]. A method for fast simulation of integrated circuits using multiblock model reduction is discussed in [29].…”

Section: Introductionmentioning

confidence: 99%

Adaptive multi‐resolution framework for fast simulation of power electronic circuits

Khan

Bazaz

Nahvi

2020

IET Circuits, Devices & Systems

View full text Add to dashboard Cite

An adaptive multi-resolution simulation (AMRS) framework for fast and accurate simulation of high-fidelity models of power electronic circuits (PECs) is presented in this study. The wide span of eigenvalues makes PEC simulation prohibitively slow. This problem can be tackled using the proposed approach. Singular perturbation approximation is used to extract simplified models by ignoring the transient contribution of the non-dominant eigenvalues. Simplified models of different orders or resolutions, each corresponding to a particular level of accuracy are derived on the fly at no additional computational cost. The simulation engine is set such that it adaptively switches across resolutions based on a predefined tolerance during the simulation. This approach is advantageous in the sense that instead of simulating the original model over the entire time-range, a combination of the original and simplified models is simulated. The combined use of the original and the simplified models is thus shown to be a powerful tool for efficient and accurate simulation of PECs. The examples illustrated, show that the AMRS approach makes the simulation considerably faster and ensures the complete response of the system is obtained with negligible error. The method is illustrated on a Class-E amplifier and a DC-DC buck-boost converter.

show abstract

“…Essentially, a standard library of models can be created then can be coupled together using the RBE method to perform a simulation of a much larger IC. In , we have pursued this approach to model several coupled FinFET structures. The goal of this paper is to investigate different methods of applying the RBE approach to model ICs.…”

Section: Introductionmentioning

confidence: 99%

“…It is also the same technique that is used in the static condensation RBE method . The second is a discontinuous Galerkin (DG) coupling approach , which allows temperature discontinuities at the interfaces between blocks. The specific DG form used is the interior penalty (IP) method as discussed by Arnold et al .…”

Section: Introductionmentioning

confidence: 99%

Proper orthogonal decomposition‐based reduced basis element thermal modeling of integrated circuits

Meyer

Helenbrook

Cheng

2017

Numerical Meth Engineering

Self Cite

View full text Add to dashboard Cite

Summary Various reduced basis element methods are compared for performing transient thermal simulations of integrated circuits. The reduced basis element method is a type of reduced order modeling that takes advantage of repeated geometrical features. It uses a reduced set of basis functions to approximate the solution of a PDE on subdomains (blocks); then these blocks are coupled together to perform a simulation on an entire domain. As the simulations are transient, a proper orthogonal decomposition basis is used, and the proper orthogonal decomposition eigenvalues from each block are used to derive error bounds for the entire simulation. These bounds are used to examine choices of block decompositions for a simplified integrated circuit structure. A decomposition that uses a single block for each transistor device is compared with a decomposition that uses one block for multiple devices. It was found that larger blocks are more computationally efficient; however, the advantage decreases if the devices within a block receive independent signals. Continuous and discontinuous methods of coupling the blocks were also compared. The coupling methods lend themselves to different solution approaches such as static condensation (continuous coupling) and block‐based inversion (discontinuous). Static condensation yielded the best convergence rate, accuracy, and operation count. Copyright © 2017 John Wiley & Sons, Ltd.

show abstract

Fast Thermal Simulation of FinFET Circuits Based on a Multiblock Reduced-Order Model

Cited by 22 publications

References 35 publications

Benchmarking of Machine Learning Methods for Multiscale Thermal Simulation of Integrated Circuits

Benchmarking of Machine Learning Methods for Multiscale Thermal Simulation of Integrated Circuits

Adaptive multi‐resolution framework for fast simulation of power electronic circuits

Proper orthogonal decomposition‐based reduced basis element thermal modeling of integrated circuits

Contact Info

Product

Resources

About