IngentaConnect Prediction of Surface Roughness Using X-ray Photoelectron Spectro...

Authors: Kim, Byungwhan¹; Park, Min-Geun¹

Source: Applied Spectroscopy, Volume 60, Issue 10, Pages 246A-272A and 1097-1216 (October 2006) , pp. 1192-1197(6)

Publisher: Society for Applied Spectroscopy

Abstract:

A new model for controlling plasma processes was constructed by combining atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), and neural networks. The applicability of XPS to modeling etch rate was also investigated, as well as the impact of dc bias inclusion. The back-propagation neural network was used to find complex relationships between XPS and AFM data. This technique was evaluated with the etching data characterized by a 2⁴ full factorial experiment. Five prediction models of surface roughness were constructed and compared. The Type I model refers to the model constructed with conventional process parameters. The Type II and III models were built with XPS and XPS plus dc bias data, respectively. The remaining Type IV and V models refer to those constructed with principal component analysis (PCA) reduced-XPS and PCA reduced-XPS plus dc bias, respectively. Mode prediction performance was evaluated as a function of training factor. In predicting the surface roughness, the Type II model yielded an improved prediction of 39% with respect to the Type IV model. The improvement was also demonstrated in modeling the etch rate. These results indicate that utilizing full XPS data is more effective for improving the model prediction performance. The advantage of XPS data was more conspicuous in constructing the surface roughness model.

Keywords: X-RAY PHOTOELECTRON SPECTROSCOPY; XPS; SURFACE ROUGHNESS; NEURAL NETWORKS; ATOMIC FORCE MICROSCOPY; AFM; PLASMA PROCESS; MODEL

Document Type: Research article

DOI: 10.1366/000370206778664554

Affiliations: 1: Department of Electronic Engineering, Sejong University, 98, Goonja-Dong, Kwangjin-Gu, Seoul, 143-747, Korea

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Prediction of Surface Roughness Using X-ray Photoelectron Spectroscopy and Neural Networks