Implementation of a robust virtual metrology for plasma etching through effective variable selection and recursive update technology

Virtual metrology (VM) is attracting much interest from semiconductor manufacturers because of its potential advantages for quality control. Plasma etching equipment with state-of-the-art plasma sensors are attractive for implementing VM. However, the plasma sensors requiring physical understanding make it difficult to select input parameters for VM. In addition, those sensors with high sensitivity frequently cause several issues in terms of VM performance. This paper will address plasma sensor issues in implementing a robust VM, where self-excited electron resonance spectroscopy, optical emission spectroscopy, and VI-probe are utilized for critical dimension prediction in a plasma etching process. An optimum sensor selection technique which can give insight into effectiveness of plasma sensors is introduced. In this technique, a numerical criterion, integrated squared response, is proposed for effective selection of important sensors for particular manipulated variables. Sensor data shift across equipment preventive maintenance (PM) and its impact on VM performance are also addressed, where a recursive data centering technique is introduced to handle PM-to-PM sensor data drift in a cost-effective way. The application of the technique introduced in this paper is shown to be effective in dynamic random access memory manufacturing. Hopefully, these results will encourage further implementation of robust virtual metrology in plasma etching for semiconductor manufacturing.