Multitask learning for virtual metrology in semiconductor manufacturing systems

Abstract

Abstract Virtual metrology (VM) estimates the real metrology of wafers from process data collected from multiple chambers. In semiconductor manufacturing, independent models for each process chamber are limited because the number of sampled wafers measured at each chamber are too few to build a reliable model. One potential solution to this problem is to pool the data from all chambers to create a model capable of learning and serving as a global predictive model. However, even with chambers that perform the same operation, the condition of their semiconductor tools may vary because of various factors. This study uses, for the first time, various multitask methods to develop VM models. By learning multiple related tasks simultaneously, multitask methods effectively increase the number of observations included in the prediction model. In addition, by identifying the related task, the method can make a prediction using only similar tasks. This property of multitask learning can be useful to account for lack of information in a single chamber and for diversity among the chambers. The experimental results indicate that multitask models consistently outperformed independent and pooled models regardless of the size of the training set used. Among the multitask methods, a multitask tree-based ensemble model outperformed the others in every case. This implies that the problem of wafer quality prediction can be better addressed with a form of multitask learning.