Vacuum Leak Detection Method Using Index Regression and Correction for Semiconductor Equipment in a Vacuum Chamber

Abstract

In semiconductor manufacturing, fault detection is an important method for monitoring equipment condition and examining the potential causes of a fault. Vacuum leakage is considered one of the major faults that can occur in semiconductor processing. An unnecessary O2 and N2 mixture, a major component of the atmosphere, creates unexpected process results and hence drops in yield. Vacuum leak detection systems that are currently available in the vacuum industry are based on helium mass spectrometers. They are used for detecting the vacuum leakage at the sole isolation condition where the chamber is fully pumped but cannot be used for in situ detection while the process is ongoing in the chamber. In this article, a chamber vacuum leak detection method named Index Regression and Correction (IRC) is presented, utilizing common data which were gathered during normal chamber operation. This method was developed by analyzing a simple list of data, such as pressure, the temperature of the chamber body, and the position of the auto pressure control (APC), to detect any leakages in the vacuum chamber. The proposed method was experimentally verified and the results showed a high accuracy of up to 97% when a vacuum leak was initiated in the chamber. The proposed method is expected to improve the process yield of the chamber by detecting even small vacuum leakages at very early stages of the process.