Statistical Process Control for Monitoring the Particles With Excess Zero Counts in Semiconductor Manufacturing

2019 ◽  
Vol 32 (1) ◽  
pp. 93-103 ◽  
Author(s):  
Wenxing Tian ◽  
Hailong You ◽  
Chunfu Zhang ◽  
Sheng Kang ◽  
Xinzhang Jia ◽  
...  
2012 ◽  
Vol 59 (2) ◽  
Author(s):  
Nor Kamaliana Khamis ◽  
Baba Md Deros ◽  
Nizaroyani Saibani ◽  
Syamsinar Baizura Ahmad Sabki

The use of Statistical Process Control (SPC) in the manufacturing process has been historically proven to increase the quality of the product. Recent trends show that companies are becoming increasingly reliant on computer based-SPC because it can save a significant amount of time compared with traditional SPC. In addition, labor-intensive tasks, such as manual data collection and entry, can be eliminated, thus reducing human error. This paper aims to prove the benefits of computer based system for SPC known as e-SPC in a semiconductor manufacturing environment. Specifically, this paper will present the case study‟s finding that show how one semiconductor manufacturing company‟s use of e-SPC can detect a process abnormality at an early stage and in real time compared with manual SPC. The case study involves interviews with the company representatives and observations on the manufacturing environment. This paper will also show how e-SPC can be used to control and then to stabilize the manufacturing operation. In conclusion, this paper demonstrates that e-SPC can significantly improve the performance of a manufacturing environment. Moreover, this paper can also be used as a reference for the implementation of e-SPC in any company.


2009 ◽  
Vol 2 (4) ◽  
pp. 246-254 ◽  
Author(s):  
Tomoaki KUBO ◽  
Tomomi INO ◽  
Kazuhiro MINAMI ◽  
Masateru MINAMI ◽  
Tetsuya HOMMA

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Manhee Lee ◽  
Dongwon Kim ◽  
Tae-Young Heo ◽  
Taewon Park ◽  
Wonjung Kim ◽  
...  

Abstract In the chemical mechanical polishing process of semiconductor manufacturing, the concentration of ‘large’ particles ($$\ge $$ ≥ 0.5 μm) in the slurry, which is considerably larger in size than the main abrasives ($$\approx $$ ≈ 0.1 μm), is a critical parameter that strongly influences manufacturing defects, yields, and reliabilities of large-scale-integrated circuits. Various instruments, so-called particle counters, based on light scattering, light extinction, and holography techniques have been developed to measure and monitor the large particle concentration in semiconductor fabs in real time. However, sizeable fluctuation in the measured particle concentration complicates the statistical process control in the fabs worldwide. Here, we show that an inherent fluctuation exists in the counting of large particles, which is universal, independent of instrument type, and quantitatively determined by the instrument’s operation parameters. We analytically derive a statistical theory of the fluctuation based on Poisson statistics and validate the theory through experiments and Monte-Carlo simulation. Furthermore, we provide a strategy to enhance the measurement accuracy by statistically adjusting the instrumental parameters commonly involved in the particle counters. The present results and analyses could be useful for statistical process control in semiconductor fabs to prevent large particle-induced defects such as micro-scratches and pits on wafers.


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