A 3-tier cooperative control architecture for multi-step semiconductor manufacturing process

Mao, Ziqiang; Kang, Wei; Wang, Frank; Raulefs, Peter

doi:10.1016/j.jprocont.2008.04.003

Cited by 2 publications

(2 citation statements)

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“…Currently, each process step in semiconductor manufacturing is controlled using Statistical Process Control(SPC) and Advanced Process Control (APC) methods to meet a pre-defined target or a set of specification limits preset in the process development. The SPC method has been used for decades to monitor the process capability and stability, such as, process drift trending and Out-of-Control (OOC) events [3] [3]. While SPC is able to detect and identify whether the production process is in the normal operation this method cannot provide the appropriate solutions for the field control and thus field engineers have been required during the manufacturing process.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, SPC and APC only work locally at each individual process step and are not suitable to multiple process steps. Mao(2008) proposed a 3-tier cooperative control architecture which enables optimization of the product quality, production improvements, best tool performance and reduction in throughput time [3]. introduced a general framework for the non-threaded state estimation methods, which are based on the Best Linear Unbiased Estimate (BLUE) of a simplified stationary singular Gauss-Markov process [4].…”

Section: Introductionmentioning

confidence: 99%

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A Key Parameters Analysis Method of the Quality Control in the Semiconductor Multiple Manufacturing Processes Based on Functional Data Analysis Method

Liu¹,

Zhang²,

Shi³

2010

AMR

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The quality of the semiconductor products is defined by a series of the key performance parameters which have some certain relations to the electronic test parameters generated among the multiple manufacturing processes. Aimed at the quality control problem of the multiple manufacturing processes, a FDA (functional data analysis) method has been used and got the mapping relationship between the process parameters of the product lines and the product quality characteristic. A simple Change-Point hypothesis has been tested to analyze the data curves generated by the FDA method, and the key process variables have been found. Then, the equalization between the new test result and the old one has been verified by the Kolmogorov-Smirnov 2-sample test method. Some multiple manufacturing processes test data, which was collected from a semiconductor product workshop, has been modeled and analyzed. And the analysis results can illustrate the key factors of the process quality control in the multiple manufacturing processes and approach the reduction of the test times and the improvement of the efficiency and effectiveness of the equipment.

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Section: Introductionmentioning

confidence: 99%