Parallel approach of Schrödinger-based quantum corrections for ultrascaled semiconductor devices

Autor: Gabriel Espiñeira, Antonio J. García-Loureiro, Natalia Seoane
Přispěvatelé: Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Información, Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
Rok vydání: 2021
Předmět:
Zdroj: Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
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ISSN: 1572-8137
1569-8025
Popis: In the current technology node, purely classical numerical simulators lack the precision needed to obtain valid results. At the same time, the simulation of fully quantum models can be a cumbersome task in certain studies such as device variability analysis, since a single simulation can take up to weeks to compute and hundreds of device configurations need to be analyzed to obtain statistically significative results. A good compromise between fast and accurate results is to add corrections to the classical simulation that are able to reproduce the quantum nature of matter. In this context, we present a new approach of Schrödinger equation-based quantum corrections. We have implemented it using Message Passing Interface in our in-house built semiconductor simulation framework called VENDES, capable of running in distributed systems that allow for more accurate results in a reasonable time frame. Using a 12-nm-gate-length gate-all-around nanowire FET (GAA NW FET) as a benchmark device, the new implementation shows an almost perfect agreement in the output data with less than a 2% difference between the cases using 1 and 16 processes. Also, a reduction of up to 98% in the computational time has been found comparing the sequential and the 16 process simulation. For a reasonably dense mesh of 150k nodes, a variability study of 300 individual simulations can be now performed with VENDES in approximately 2.5 days instead of an estimated sequential execution of 137 days Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature SI
Databáze: OpenAIRE
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