Article
Vacuum Leak Detection Method Using Index Regression and
Correction for Semiconductor Equipment in a Vacuum Chamber
Taekyung Ha
1,2
and Hyunjung Shin
1,
*
Citation: Ha, T.; Shin, H. Vacuum
Leak Detection Method Using Index
Regression and Correction for
Semiconductor Equipment in a
Vacuum Chamber. Appl. Sci. 2021, 11,
11762. https://doi.org/10.3390/
app112411762
Academic Editors: Paolo Renna and
João Carlos de Oliveira Matias
Received: 27 October 2021
Accepted: 6 December 2021
Published: 10 December 2021
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1
Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si 16499,
Gyeonggi-do, Korea; tkh@ajou.ac.kr
2
R&D Center for PSK-INC Corporation, 48, Samsung 1-ro 4-gil, Hwaseong-si 18449, Gyeonggi-do, Korea
* Correspondence: shin@ajou.ac.kr
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 O
2
and N
2
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.
Keywords: vacuum leak detection; index regression; vacuum chamber; semiconductor equipment
1. Introduction
Over the past few decades, the worldwide semiconductor industry has maintained an
average growth of 15% per year. This steady growth is the result of a continuous reduction
in the cost per function of 25–30% per year [1–3].
Semiconductor manufacturing involves a highly complex and lengthy wafer fabrica-
tion process, with 300–500 process steps and a large number of interrelated variables [
4
–
6
].
To obtain substantial benefits, the semiconductor industry focuses on features related to
manufacturing technology that are required in order to maintain the high reliability and
effectiveness of the manufacturing process, such as reducing gate length, increasing wafer
size and improving yield [
7
–
9
]. However, manufacturing technology has already reached
its limit. Thus, it has been recognized that factory productivity should also be improved in
order to maintain growth while reducing the production cost [
10
,
11
]. In semiconductor
manufacturing, some process steps need to be performed continuously, without any in-
terruption in the process flow, such as etching, ashing, deposition, and lithography. The
sequence of continuous steps is called a ”run” and is performed on individual manufactur-
ing equipment. Even though a fault may occur during any step of the run, it can only be
detected after the entire run has been completed by performing wafer metrology [12–14].
As a result, data-driven fault detection has become a significant topic in the semicon-
ductor industry and is expected to provide an efficient method for predictive maintenance
that allows companies to save time and money [
15
–
20
]. Vacuum leakage is considered to
Appl. Sci. 2021, 11, 11762. https://doi.org/10.3390/app112411762 https://www.mdpi.com/journal/applsci
