We consider optimal design problems for semiconductor devices which are simulated using the energy transport model. We develop a descent algorithm based on the adjoint calculus and present numerical results for a ballistic diode. Furthermore, we compare the optimal doping profile with results computed based on the drift diffusion model. Finally, we exploit the model hierarchy and test the space mapping approach, especially the aggressive space mapping algorithm, for the design problem. This yields a significant reduction of numerical costs and programming effort.

Optimal design problems for semiconductor devices with relevant thermal effects can be formulated by help of the energy transport model. In this paper we perform a sensitivity analysis to derive the first‐order necessary condition for the optimization. Exploiting the special structure of the KKT system we use a special variant of the classical Gummel iteration to provide a very fast optimization algorithm. Numerical results for a ballistic diode underline the feasibility of our approach.

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A crucial task in designing semiconductor devices is to provide a doping profile that assures specific electrical properties. This research work is focused in redesigning doping profiles of semiconductor devices in order to obtain an increased output current; however, large doping levels can degenerate the devices and hence a trade-off between doping profile deviation and output current should be found. The doping profile optimization in semiconductor has been tackled as a multi-objective optimization problem using the Nondominated Sorting Genetic Algorithm (NSGA-II). We focus on silicon diodes and MOSFET devices; firstly, we redesign the doping profile of diodes in order to obtain a trade-off between doping profile deviation and output current. Secondly, we find a trade-off between current and temperature for a MOSFET device. The experimental results confirm the effectiveness of the proposed approach to face this class of problems in electronic design automation.