Determination of lapping parameters for silicon wafer using an artificial neural network

Savas Ozturk, Erhan Kayabasi*, Erdal Celik, Huseyin Kurt

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

An artificial neural network (ANN) simulation was utilized to determine the lapping parameters such as rotation speed, lapping duration and lapping pressure under a constant slurry supply for n-type crystalline Silicon (c-Si) wafers. Experiments were done with a Logitech PM5 lapping and polishing machine to obtain input data and target data for training, testing and validation of ANN. Lapping operation had five main parameters affecting surface quality: rotation speed, lapping duration, lapping pressure, flowrate of abrasive slurry and particle size in abrasive slurry. However, in this study slurry flowrate was assumed constant due the researches performed before. 218 lapping operations were performed with different values of the selected parameters and new lapping parameters were derived for different lapping conditions to achieve the best surface quality by using an ANN. In this study, wafers in 400 µm thickness cut under identical conditions from n-type single c-Si ingot in a STX 1202 DWS cutting machine were employed. Surface roughness (Ra) values were measured three times from different points of the wafers after lapping with a contact type surface roughness measurement tool using a microscopic scale stylus profiler (SP). In ANN simulation 70% of Ra values were utilized for training, 15% of Ra values were utilized for validation and 15% of Ra values were utilized for test data. Results obtained from ANN simulation validated with a success above 99%.

Original languageEnglish
Pages (from-to)260-270
Number of pages11
JournalJournal of Materials Science: Materials in Electronics
Volume29
Issue number1
DOIs
Publication statusPublished - 1 Jan 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2017, Springer Science+Business Media, LLC.

Fingerprint

Dive into the research topics of 'Determination of lapping parameters for silicon wafer using an artificial neural network'. Together they form a unique fingerprint.

Cite this