SHEWMA: an end-of-line SPC scheme using wafer acceptance test data

Fan, Chia-Ming; Guo, Ruey‐Shan; Chang, Soon-Jyh; Chen, Wei

doi:10.1109/66.857945

Cited by 19 publications

(5 citation statements)

References 21 publications

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“…which is an unbiased estimate of the true covariance matrix in case of multinormally distributed data. Consider the fault modeling described by (1) and (2), it follows that:…”

Section: Fault Amplitude Estimationmentioning

confidence: 99%

“…The derivation of ψ −1 (y) denoted Z(y) = ψ −1 (y) ′ is : Z(y) = 4a 1 a 2 2 y 4 + 2(a 2 1 a 2 + aa 2 2 )y 3 + 3aa 1 a 2 y 2 + 2aa 2 1 y a(a + a 1 y + a 2 y 2 ) 2 .…”

Section: Probability Density Function Of the Fault Amplitude Estimatementioning

confidence: 99%

“…The detection of a particular fault is based on checking whether the current measurements are statistically different from the a priori known faultless measurements. Detection indices with control charts are designed to this end [2][3][4]. The MEWMA (Multivariate Exponentially Weighted Moving Average) and the MCUSUM (Multivariate Cumulative Sum) are able to detect deviations related to the process mean vector [5].…”

Section: Introductionmentioning

confidence: 99%

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Incipient fault amplitude estimation using KL divergence with a probabilistic approach

2016

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International audienceThe Kullback–Leibler (KL) divergence is at the centre of Information Theory and change detection. It is characterized with a high sensitivity to incipient faults that cause unpredictable small changes in the process measurements. This work yields an analytical model based on the KL divergence to estimate the incipient fault magnitude in multivariate processes. In practice, the divergence has no closed form and it must be numerically approximated. In the particular case of incipient fault, the numerical approximation of the divergence causes many false alarms and missed detections because of the slight effect of the incipient fault. In this paper, the ability and relevance to estimate the incipient fault amplitude using the numerical divergence is studied. The divergence is approximated through the calculation of discrete probabilities for faultless and faulty signals. The estimation results that are obtained by simulation induce an error lower than 1% on the fault amplitude

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“…which is an unbiased estimate of the true covariance matrix in case of multinormally distributed data. Consider the fault modeling described by (1) and (2), it follows that:…”

Section: Fault Amplitude Estimationmentioning

confidence: 99%

“…The derivation of ψ −1 (y) denoted Z(y) = ψ −1 (y) ′ is : Z(y) = 4a 1 a 2 2 y 4 + 2(a 2 1 a 2 + aa 2 2 )y 3 + 3aa 1 a 2 y 2 + 2aa 2 1 y a(a + a 1 y + a 2 y 2 ) 2 .…”

Section: Probability Density Function Of the Fault Amplitude Estimatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Incipient fault amplitude estimation using KL divergence with a probabilistic approach

2016

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“…One possible treatment is to combine multiple charts to take care shifts in all ranges. For example, applying Shewhart control limits to EWMA or CUSUM charts to detect both large and small shifts (Lucas (1982), Albin et al (1997), Chih-Min et al (2000), Reynolds and Stoumbos (2005)). …”

Section: Detecting Unknown or Mixed-range Shiftsmentioning

confidence: 99%

Adaptive Charting Techniques: Literature Review and Extensions

Tsung

Wang

2010

Frontiers in Statistical Quality Control 9

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Summary. The continuous development of SPC is driven by challenges arising from practical applications across diverse industries. Among others, adaptive charts are becoming more and more popular due to their capability in tackling these challenges by learning unknown shifts and tracking time-varying patterns. This chapter reviews recent development of adaptive charts and classifies them into two categories: those with variable sampling parameters and those with variable design parameters. This review focuses on the latter group and compares their charting performance. As an extension to conventional multivariate charts, this work proposes a double-sided directionally variant chart. The proposed chart is capable of detecting shifts having the same or opposite directions as the reference vector and is more robust to processes with unpredictable shift directions.

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“…The front-end process includes wafer fabrication (WF) and wafer probing. During WF, various test structures are fabricated on a wafer to extract information on the process and device performance for yield management [1]. Once WF is completed, the wafer acceptance test (WAT) is conducted using these test structures to measure important process related parameters, such as contact resistance, threshold voltage and diode leakage current etc.…”

Section: Introductionmentioning

confidence: 99%

A Gaussian Mixture Model Clustering Ensemble Regressor for Semiconductor Manufacturing Final Test Yield Prediction

2021

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In the semiconductor industry, many studies have been carried out for front-end related process improvement and yield prediction using machine learning techniques. However, very few research investigations have dealt with the backend Final Test (FT) yield prediction using the front-end wafer acceptance test (WAT) parameters. The manufacturing cycle time between wafer fabrication (WF) and FT can range anywhere between a few weeks to several months. It is therefore important for semiconductor manufacturers to detect wafer material related low yield problems at an earlier stage for effective cost and quality control. This is a challenging goal as the input data used for prediction is at a very early manufacturing stage and the output FT yield for packaged chips is the last stage of the fabrication chain. There are many unknown production variations caused by different manufacturing processes, equipment configurations and human interferences in this multi-stage sequential fabrication chain. In this paper, we proposed a novel procedure to predict the backend FT yield at the WF stage itself using a Gaussian Mixture Models (GMM) clustering approach that is applied to build a weighted ensemble regressor. Real production data for new chip product lines are verified with this method and show significant improvement in the prediction performance.

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SHEWMA: an end-of-line SPC scheme using wafer acceptance test data

Cited by 19 publications

References 21 publications

Incipient fault amplitude estimation using KL divergence with a probabilistic approach

Incipient fault amplitude estimation using KL divergence with a probabilistic approach

Adaptive Charting Techniques: Literature Review and Extensions

A Gaussian Mixture Model Clustering Ensemble Regressor for Semiconductor Manufacturing Final Test Yield Prediction

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