Case of small-data analysis for ion implanters in the era of big-data FDC

This paper presents a case study of constructing process models based on physical mechanisms of semiconductor manufacturing tools in attempts to predict behaviours of process conditions. Actual measurements from the processing tools are always corrupted with noises and crunching huge volumes of temporal traces of status variables very often fail to pinpoint the accurate fault conditions, not to mention any of their efficient classifications, should abnormal conditions really exist. The current fashion of moving into massive big data computing is yet to distill concrete correlations among tool conditions and impacts on process results of semiconductor devices. As an alternative before the foolproof maturity of big data cracking, and in contrast to the conventional black-box approach of statistical regressions, we take a fundamental view in constructing physical model of the ion implantation process for a flywheel implanter, first to calculate the motion trajectories and subsequently, the implantation dosage on the wafer. We summarize the underlying solution techniques in principles and leave the specific details of parameter calibrations to individual field practitioners.