Determination of lapping parameters for silicon wafer using an artificial neural network
Küçük Resim Yok
Tarih
2018
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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 A mu m thickness cut under identical conditions from n-type single c-Si ingot in a STX 1202 DWS cutting machine were employed. Surface roughness (R (a) ) 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 R (a) values were utilized for training, 15% of R (a) values were utilized for validation and 15% of R (a) values were utilized for test data. Results obtained from ANN simulation validated with a success above 99%.
Açıklama
Anahtar Kelimeler
Finite-Element-Analysis, Dielectric-Constant, Surface-Roughness, Solar-Cell, Performance, Removal, Wear
Kaynak
Journal of Materials Science-Materials in Electronics
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
29
Sayı
1
