Browsing by Author "Cheng, Ya-Ching"

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An Improved Approach for Estimating Product Performance Based on the Capability Index C(pmk)
(2009-01-01)
Pearn, W. L.
;
Yang, Dong-Yuh
;
Cheng, Ya-Ching
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
In this article, we construct an improved procedure for estimating the process capability index C(pmk). We propose a new Cpmk lower-bound approach based on the GCI concept, and compare it with other existing methods. Based on the comparison results, we conclude with a recommendation, and construct a step-by-step procedure for the recommended approach to estimate the actual process capability Cpmk for various sample sizes. The lower bound attended by our recommended approach, indeed, improves other existing lower bound methods. We also investigate a real-world application to illustrate how we could apply the recommended approach to the actual manufacturing processes.
An Improved Approach for Estimating Product Performance Based on the Capability Index C-pmk
(2009-01-01)
Pearn, W. L.
;
Yang, Dong-Yuh
;
Cheng, Ya-Ching
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
In this article, we construct an improved procedure for estimating the process capability index C-pmk. We propose a new Cpmk lower-bound approach based on the GCI concept, and compare it with other existing methods. Based on the comparison results, we conclude with a recommendation, and construct a step-by-step procedure for the recommended approach to estimate the actual process capability Cpmk for various sample sizes. The lower bound attended by our recommended approach, indeed, improves other existing lower bound methods. We also investigate a real-world application to illustrate how we could apply the recommended approach to the actual manufacturing processes.
Measuring production yield for processes with multiple characteristics
(2010)
Pearn, W. L.
;
Cheng, Ya-Ching
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Numerous capability indices have been proposed to measure the performance of processes with multiple characteristics. The index [image omitted] provides an exact measure on the production yield of multinormal processes in which the characteristics are mutually independent. In this paper, we thoroughly investigate the relationship between process parameters and the sampling distribution of [image omitted]. Our investigation shows that for a fixed [image omitted], the variance of sample estimator of [image omitted] is restricted in an interval. For reliability consideration, the maximal variance is used in the estimation and testing of the production yield to ensure the level of confidence. Also, information about sample sizes required for specified precision of estimation and for convergence is determined. At last, we implement a trivariate process with data collected from a plastics manufacturing industrial to demonstrate the practicability of the proposed method in measuring the production yield.
Predicting Shot-Level SRAM Read/Write Margin Based on Measured Transistor Characteristics
(2016-02-01)
Bin, Shu-Yung
;
Lin, Shih-Feng
;
Cheng, Ya-Ching
;
Liau, Wen-Rong
;
Hou, Alex
An SRAM-array test structure provides the capability of directly measuring the characteristics of each transistor and the read/write metrics for each static random access memory (SRAM) cell in the array. However, the total test time of measuring the read/write metrics takes longer than that of measuring each transistor\'s characteristics. This paper presents a model-fitting framework to predict the average read/write metrics of the SRAM cells in a lithography shot using only the measured transistor characteristics. The proposed framework is validated through the measurement result of 4750 samples of a 128-bit SRAM-array test structure implemented in a United Microelectronics Corporation 28-nm process technology. The experimental results show that the learned models can achieve at least 97.77% R-square on fitting the shot-level read static noise margin, write margin, and read current based on 2375-sample testing data.