Machine learning aided process control: critical dimension uniformity control of etching process in 1z nm DRAM

Conventional semiconductor etching process control has been performed by separated steps: process, metrology, and feedback control. Uniformity of structures such as Critical Dimension (CD) is an important factor in determining completeness of etching process. To achieve better uniformity, several feedback control has been performed. However, it is difficult to give feedback to the process after metrology due to the lack of process knowledge. In this study, we propose a machine learning technique that can create process control commands from the measured structure using a miniaturized Integrated Metrology (IM) of Spectroscopic Ellipsometery (SE) form. And it is possible to learn the physical analysis through machine learning without introducing a physical analysis method. The proposed analysis consists of two machine learning part: the first neural network for CD metrology, and second network for command generation. The first neural network takes a spectrum sampled at 2048 wavelengths obtained from IM as an input, and outputs CDs of structures. Finally, the second artificial neural network takes a changes of temperatures in a wafer and outputs the control commands of powers. As a result, we have improved the CD range of poly mask in a wafer from 1.69 nm to 1.36 nm.