A study on global and local optimization techniques for TCAD analysis tasks

Authors:
T. Binder;C. Heitzinger;S. Selberherr
Affiliations:
Inst. for Microelectron., Tech. Univ. of Vienna, Austria;-;-
Venue:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Year:
2006

Citing 0
Cited 5

Classification of analog synthesis tools based on their architecture selection mechanisms

Integration, the VLSI Journal
Numerical Simulation of Static Noise Margin for a Six-Transistor Static Random Access Memory Cell with 32nm Fin-Typed Field Effect Transistors

ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Electronic design automation using a unified optimization framework

Mathematics and Computers in Simulation
A distributed simulation-based computational intelligence algorithm for nanoscale semiconductor device inverse problem

ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
Nano-CMOS thermal sensor design optimization for efficient temperature measurement

Integration, the VLSI Journal

Quantified Score

Hi-index	0.03

Visualization

Abstract

We evaluate optimization techniques to reduce the necessary user interaction for inverse modeling applications as they are used in the technology computer-aided design field. Four optimization strategies are compared. Two well-known global optimization methods, simulated annealing and genetic optimization, a local gradient-based optimization strategy, and a combination of a local and a global method. We rate the applicability of each method in terms of the minimal achievable target value for a given number of simulation runs and in terms of the fastest convergence. A brief overview over the three used optimization algorithms is given. The optimization framework that is used to distribute the workload over a cluster of workstations is described. The actual comparison is achieved by means of an inverse modeling application that is performed for various settings of the optimization algorithms. All presented optimization algorithms are capable of evaluating several targets in parallel. The best optimization strategy that is found is used in the calibration of a model for silicon self-interstitial cluster formation and dissolution.