Prediction of planarization property in copper film chemical mechanical polishing via response surface methodology and convolutional neural network

Abstract

Chemical mechanical polishing (CMP) is one of the most important and effective technologies to achieve global planarization for precision machining of the wafer surface. The planarization property of slurry is an essential index in evaluating the quality of the copper (Cu) film CMP process. Prediction of the planarization property is of vital significance for product quality control during the CMP process. Data‐driven approaches to predict results based on response surface methodology (RSM) and neural network (NN) to pursue better prediction performance were proposed in this paper. In our design, all three optimization methods (RSM, NN, RSM + NN) are proved to be accurate and reliable, which can predict the experimental results with finer grid accuracy under the condition of only 17 test data points. In particular, the prediction accuracy of RSM + NN method is higher than that of the other two methods, and the error is only 0.16 %. Notably, the results demonstrate that such less time‐consuming optimization methods can realize the acquisition of more desirable CMP process parameters and compositions of slurries, and the versatility and simplicity of our methods can also potentially provide an alternative experiment design concept for a wider range of applications.

Optimization of electrode preparation conditions by response surface methodology for improved formic acid electrooxidation on Pd/MWCNT/GCE

“…5a, the experimental points are scattered around the diagonal. The scattering of the experimental data around the diagonal reveals that the proposed model represents the experimental points well [30]. Figure 5b shows the internally studentized residual vs. normal probability distribution.…”