Abstract
A method was proposed for analyzing the optical glass lens centering process, and experiments on biplane quartz lenses were performed to determine the material removal rate (MRR) for the hard, brittle material. This study used acoustic emission–sensing technology to monitor the MRR and reconstruct the original shape of the lens. The MRR was evaluated, and an error of 17.87% was obtained. A Taguchi experiment was combined with signal analysis to optimize the process parameters, and a support-vector machine was trained to classify the quality of the grinding wheel; the model had accuracy 98.8%. By using the proposed analysis method, workpiece quality was controlled to an edge surface roughness of < 2 μm, a lens circularity error of < 0.01 mm, a crack length of < E0.1, and an optical axis error of < 150 μrad.
Highlights
Biplane quartz lens is an important auxiliary-material in semiconductor manufacturing processes
The acoustic emission (AE) signal was positively correlated with the material removal rate (MRR); that is, the cumulative value of the AE signal was relative to the amount of material removed from the lens
The correction constant and the scale from the AE signal to the removal rate were evaluated on the basis of the length ratio a:b:c, which is ideally 1:0.1725:0.2145
Summary
Biplane quartz lens is an important auxiliary-material in semiconductor manufacturing processes. Biplane quartz lenses are a crucial auxiliary material in semiconductor manufacturing processes Because it has high purity (impurity < 10 ppm), a low thermal expansion coefficient (5.11–5.8 × 10−7/K), high temperature resistance (softening point of approximately 1,700 °C), high resistance to acids and alkalis, a low refractive index (1.45), low dispersion (Abbe number = 68), and good light transmittance in the infrared to ultraviolet bands, quartz is often used in experimental equipment and high-precision measuring instruments. The time and frequency features of signals can be used as reference parameters of a support vector machine (SVM) to perform binary classification that helps judging certain conditions, such as end point detection [17], tool breakage [18], grinding burns [19,20,21] and surface roughness monitoring. An SVM model was created to evaluate the wear caused by the grinding wheel by classifying grinding wheel condition into “normal” or “worn.”
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