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Micromachines 2018, 9(11), 550;

Accelerating Flux Calculations Using Sparse Sampling

Christian Doppler Laboratory for High Performance TCAD, Institute for Microelectronics, TU Wien, Vienna 1040, Austria
Silvaco Europe Ltd., St. Ives PE27 5JL, UK
Institute for Microelectronics, TU Wien, Vienna 1040, Austria
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Lecture Notes in Computer Science, Volume 10860, Proceedings of the International Conference on Computational Science (ICCS)—Part I, Wuxi, China, 11–13 June 2018; Springer: Cham, Switzerland, 2018; pp. 694–707
Received: 20 September 2018 / Revised: 18 October 2018 / Accepted: 23 October 2018 / Published: 26 October 2018
(This article belongs to the Special Issue Miniaturized Transistors)
PDF [4469 KB, uploaded 26 October 2018]


The ongoing miniaturization in electronics poses various challenges in the designing of modern devices and also in the development and optimization of the corresponding fabrication processes. Computer simulations offer a cost- and time-saving possibility to investigate and optimize these fabrication processes. However, modern device designs require complex three-dimensional shapes, which significantly increases the computational complexity. For instance, in high-resolution topography simulations of etching and deposition, the evaluation of the particle flux on the substrate surface has to be re-evaluated in each timestep. This re-evaluation dominates the overall runtime of a simulation. To overcome this bottleneck, we introduce a method to enhance the performance of the re-evaluation step by calculating the particle flux only on a subset of the surface elements. This subset is selected using an advanced multi-material iterative partitioning scheme, taking local flux differences as well as geometrical variations into account. We show the applicability of our approach using an etching simulation of a dielectric layer embedded in a multi-material stack. We obtain speedups ranging from 1.8 to 8.0, with surface deviations being below two grid cells (0.6–3% of the size of the etched feature) for all tested configurations, both underlining the feasibility of our approach. View Full-Text
Keywords: flux calculation; etching simulation; process simulation; topography simulation flux calculation; etching simulation; process simulation; topography simulation

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Gnam, L.; Manstetten, P.; Hössinger, A.; Selberherr, S.; Weinbub, J. Accelerating Flux Calculations Using Sparse Sampling. Micromachines 2018, 9, 550.

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