Data Mining using Genetic Programming for Construction of a Semiconductor Manufacturing Yield Rate Prediction System

Li, Te-Sheng; Huang, Cheng-Lung; Wu, Zhixian

doi:10.1007/s10845-005-0008-7

Cited by 45 publications

(10 citation statements)

References 10 publications

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“…Recently Shimada and Sakurai (2003) proposed an accurate analytical yield forecasting approach for system-large-scale-integration (LSI) embedded memories based on the failure-related method to make it possible to treat both asymmetric repair and link set. Li, Huang, and Wu (2006) proposed a genetic programming (GP) approach that attempted to predict the yield of a job in a semiconductor fabrication plant according to the in-line inspection data of the job. On the other hand, human factors in the learning process often complicate the situation, and expert opinions are valuable and effective in handling this situation (Watada et al, 1986).…”

Section: Introductionmentioning

confidence: 99%

Enhancing the yield competitiveness of a semiconductor fabrication factory with dynamic capacity re-allocation

Chen

2009

Computers & Industrial Engineering

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Section: Introductionmentioning

confidence: 99%

Enhancing the yield competitiveness of a semiconductor fabrication factory with dynamic capacity re-allocation

Chen

2009

Computers & Industrial Engineering

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“…From the MiYM viewpoint, recently Shimada and Sakurai [14] proposed an accurate analytical yield forecasting approach for system-large-scale-integration (LSI) embedded memories based on the failure-related method to make it possible to treat both asymmetric repair and link set. Li et al [15] proposed a genetic programming (GP) approach that attempted to predict the yield of a job in a semiconductor fabrication plant according to the in-line inspection data of the job. Chen and Lin [16] proposed a two-step approach for semiconductor yield prediction, in which multiple experts constructed their own fuzzy yield learning models from various viewpoints to predict the yield of a product.…”

Section: Yield Learning Modellingmentioning

confidence: 99%

Establishing the optimal and efficient capacity re-allocation plans for enhancing the long-term competitiveness of a semiconductor product — a long-term trend viewpoint

Chen

2010

Proceedings of the Institution of Mechanical Engineers, Part B:

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Yield is undoubtedly the most critical factor to the competitiveness of a product in a semiconductor manufacturing factory. Therefore, evaluating the competitiveness of a product with its yield is a reasonable idea. For this purpose, Chen's approach is extended in this study to evaluate the long-term competitiveness of a product based on its yield learning model from a new viewpoint -the trend in the mid-term competitiveness. Subsequently, to enhance the long-term competitiveness of a product, a non-linear programming approach is proposed to optimize the effects of capacity re-allocation. A practical example is used to demonstrate the proposed methodology. Experimental results show that with an additional capacity of 16 780 wafers per month, the long-term competitiveness of the product is maximized. Also, the most efficient way is to allocate 14 500 more wafers per month to the product. These results are helpful in making capacity re-allocation decisions.

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“…Recently, Shimada and Sakurai [9] proposed an accurate analytical yield forecasting approach for system-large-scale-integration (LSI) embedded memories based on the failure-related method to make it possible to treat both asymmetric repair and link set. Li et al [10] proposed a genetic programming (GP) approach that attempted to predict the yield of a wafer lot according to the in-line inspection data of the lot. In a related field, Lin et al [11] constructed a grey forecasting model to forecast abnormal quality characteristics in a silicon wafer slicing process.…”

Section: Some Existing Yield Learning Modelsmentioning

confidence: 99%

A Fuzzy Logic Approach for Incorporating the Effects of Managerial Actions on Semiconductor Yield Learning

Chen

2007

2007 International Conference on Machine Learning and Cybernetics

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The competition in the semiconductor industry is becoming more and more fierce, which significantly distorts the learning process of semiconductor yield improvement. For example, if the yield of a product could not reach a certain level before a given deadline, then the competitiveness of the product will disappear and capacity will be re-allocated to other products. To prevent that from happening, some managerial actions, e.g. executing a quality engineering project, quickening the speed of mass production, etc. can be taken to accelerate yield learning. After such actions, the yield learning model has to be modified. In this study, how to incorporate the effects of such managerial actions on semiconductor yield learning is investigated. Subsequently, a new fuzzy yield learning model is developed. The proposed methodology has been applied to the data of four semiconductor products. Experimental results revealed the effectiveness of the proposed methodology.

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Data Mining using Genetic Programming for Construction of a Semiconductor Manufacturing Yield Rate Prediction System

Cited by 45 publications

References 10 publications

Enhancing the yield competitiveness of a semiconductor fabrication factory with dynamic capacity re-allocation

Enhancing the yield competitiveness of a semiconductor fabrication factory with dynamic capacity re-allocation

Establishing the optimal and efficient capacity re-allocation plans for enhancing the long-term competitiveness of a semiconductor product — a long-term trend viewpoint

A Fuzzy Logic Approach for Incorporating the Effects of Managerial Actions on Semiconductor Yield Learning

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