Prediction of silicon dry etching using a piecewise linear algorithm

A piecewise linear algorithm for predicting silicon etch rates in fluorine-based plasmas is shown. Discrete experimental data of pressure and RF power in reactive ion etching are used to construct a set of local two-dimensional etching functions that serve as a basis for computing numerical solution...

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Detalles Bibliográficos
Autor: CLAUDIA REYES BETANZO
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2013
País:México
Institución:Instituto Nacional de Astrofísica, Óptica y Electrónica
Repositorio:Repositorio Institucional del INAOE
Idioma:inglés
OAI Identifier:oai:inaoe.repositorioinstitucional.mx:1009/2354
Acceso en línea:http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2354
Access Level:acceso abierto
Palabra clave:info:eu-repo/classification/Inspec/Silicon etching
info:eu-repo/classification/Inspec/Piecewise linear
info:eu-repo/classification/Inspec/Algorithm
info:eu-repo/classification/cti/1
info:eu-repo/classification/cti/22
info:eu-repo/classification/cti/2203
Descripción
Sumario:A piecewise linear algorithm for predicting silicon etch rates in fluorine-based plasmas is shown. Discrete experimental data of pressure and RF power in reactive ion etching are used to construct a set of local two-dimensional etching functions that serve as a basis for computing numerical solutions (pressure and power values for a specific predicted silicon etch rate). It must be pointed out that, although the algorithm scans the entire data domain, a testing procedure is applied to ensure that the computing task will be invoked only when a solution exists, and otherwise it will be discarded (this avoids brute force methods). In the last step of the algorithm, all solutions are collected and interpolated to construct a solution path. In order to verify the match between the experimental etching results and numerical predictions, the algorithm has been coded and tested using Maple® Release 13.0, showing a successful validation with a difference between experimental data and computed numerical solutions as low as 1% for SF₆, and 4% for SF₆/O₂ in the best case and a root-mean squared error of 0.03.