The stationary semiconductor device equations
The stationary semiconductor device equations
Zeitschrift für Angewandte Mathematik und Physik (ZAMP)
Journal of Computational Physics
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Mesh Adaptive Direct Search Algorithms for Constrained Optimization
SIAM Journal on Optimization
2D numerical simulation of the MEP energy-transport model with a finite difference scheme
Journal of Computational Physics
Multiobjective Optimization Through a Series of Single-Objective Formulations
SIAM Journal on Optimization
OrthoMADS: A Deterministic MADS Instance with Orthogonal Directions
SIAM Journal on Optimization
Algorithm 909: NOMAD: Nonlinear Optimization with the MADS Algorithm
ACM Transactions on Mathematical Software (TOMS)
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Designing high-performance semiconductor devices is a complex optimization problem, which is characterized by multiple and, often, conflicting objectives. In this research work, we introduce a multi-objective optimization design approach based on the Bi-Objective Mesh Adaptive Direct Search (BiMADS) algorithm. First, we assess the performance of the algorithm on the design of a n^+-n-n^+ silicon diode using a standard drift-diffusion model, showing that BiMADS is able to find the best solutions and to outperform the state-of-the-art algorithms. Successively, we tackle the design of MESFET and MOSFET devices, using a Maximum Entropy Principle (MEP) model; BiMADS is able to locate new designs that minimize the size of the device and provide an increased output current. Moreover, it is proved that BiMADS is able to locate promising solutions with a tight budget of objective function evaluations, which makes it suitable for large-scale industrial applications.