Nonlinear Fault Detection Based on Fault-related Multiphase Principle Polynomial Analysis for Al Stack Etch Process

In integrated circuit manufacturing industry, etch process is a complex nonlinear batch process. Al stack etch is the penultimate layer of dry etch. Based on the specific steps of the recipe, it has the multiphase characteristic and the can exhibit significantly different behaviors over different phases. However, conventional fault detection methods cannot effectively monitor Al stack etch process due to nonlinear and multiphase characteristics. Moreover, they are usually modeled by normal process data. In Al stack etch process, fault process data can be obtained from the datalog of equipments. In order to utilize these data, a novel nonlinear fault detection method called fault-related multistage principal polynomial analysis (FMPPA) is proposed in this work. FMPPA is efficient to deal with nonlinearity of the multiphase batch process. Furthermore, it can make full use of fault data by decomposing original feature space into three subspaces. FMPPA is applied to monitoring the Al stack etch process. Simulation results demonstrate that FMPPA is superior to other methods.