New challenges on semiconductor plasma manufacturing processes

Machine learning (ML) technique was applied to the plasma-enhanced atomic layer deposition (PEALD) process condition and the plasma distribution control. The latter being related to the uniformity of film thickness and etch rate within a wafer, which are important properties in semiconductor manufacturing. The ML approach adopted herein is governed by an algorithm of building regression models with tuning parameters as explanatory variables, and the uniformity of film thickness and etch rate as response variables.

Optimization of the PEALD film-thickness uniformity within a wafer was carried out by an engineer and by the ML approach, both separately. Such a wide variation of uniformity was attributed to the initial conditions. The engineer could not settle the variation of uniformity after the five trials, which was effectively settled by the ML approach. The results suggest that the ML approach can find the optimum condition quickly and settle the variation.

Once the key parameter is suggested via ML, the desired target in the extrapolation area is achievable by human judgment. Fundamentally, a learning result can be transferred when the process conditions are similar. Thus, it is efficient to build a database by using data that are relatively easy to obtain.