A distributed simulation-based computational intelligence algorithm for nanoscale semiconductor device inverse problem

Authors:
Yiming Li;Cheng-Kai Chen
Affiliations:
Department of Communication Engineering, National Chiao Tung University, Hsinchu, Taiwan;Department of Communication Engineering, National Chiao Tung University, Hsinchu, Taiwan
Venue:
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
Year:
2006

Citing 8
Cited 0

Task allocation by parallel evolutionary computing

Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
Issues in parallelizing multiobjective evolutionary algorithms for real world applications

Proceedings of the 2002 ACM symposium on Applied computing
Efficient and Accurate Parallel Genetic Algorithms

Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Nonlinear optimization and parallel computing

Parallel Computing - Special issue: Parallel computing in numerical optimization
Investigation of electrical characteristics on surrounding-gate and omega-shaped-gate nanowire FinFETs

IEEE Transactions on Nanotechnology
A Comparative Study of Electrical Characteristic on Sub-10-nm Double-Gate MOSFETs

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

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a distributed simulation-based computational intelligence algorithm for inverse problem of nanoscale semiconductor device is presented. This approach features a simulation-based optimization strategy, and mainly integrates the semiconductor process simulation, semiconductor device simulation, evolutionary strategy, and empirical knowledge on a distributed computing environment. For a set of given target current-voltage (I-V) curves of metal-oxide-semiconductor field effect transistors (MOSFETs) devices, the developed prototype executes evolutionary tasks to solve an inverse doping profile problem, and therefore optimize fabrication recipes. In the evolutionary loop, the established management server allocates the jobs of process simulation and device simulation on a PC-based Linux cluster with message passing interface (MPI) libraries. Good benchmark results including the speed-up, the load balancing, and the parallel efficiency are presented. Computed results, compared with the realistic measured data of 65 nm n-type MOSFET, show the accuracy and robustness of the method.