The standardized plant analysis risk human reliability analysis (SPAR-H) method is widely used for human reliability analysis to adjust the nominal human error probability (HEP) by assigning different multipliers to t...
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The standardized plant analysis risk human reliability analysis (SPAR-H) method is widely used for human reliability analysis to adjust the nominal human error probability (HEP) by assigning different multipliers to the performance shaping factors (PSFs). Nevertheless, SPAR-H suffers from assuming PSFs to be independent without considering any overlaps and dependencies. Therefore, this study introduces a new systematic method to analyze the relationships among the PSFs in SPAR-H qualitatively and quantitatively to obtain more reasonable HEP estimation results. The proposed method comprises three primary aspects: 1) a regularized autoencoder for the denoising and feature extraction of expert evaluation results, 2) the weighted influence non-linear gauge system- based adversarial interpretive structure modeling (WINGS-AISM) method to analyze the relationships among the PSFs and construct their causal hierarchy, and 3) a new relative weighting system to modify the PSF multipliers based on this hierarchy. The results of experiments comparing the proposed method with conventional methods highlight that our method effectively reduces the double counting of overlapping PSFs in SPAR-H, providing more reasonable and accurate HEP estimation results.
A new generation of metrology tools has been recently released to the market, allowing extensive characterization of semiconductor samples thanks to massive measurements. The resulting substantial growth of measuremen...
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ISBN:
(纸本)9781510649828;9781510649811
A new generation of metrology tools has been recently released to the market, allowing extensive characterization of semiconductor samples thanks to massive measurements. The resulting substantial growth of measurement sets in size and number of descriptors made the data analysis with the traditional statistical techniques laborious, being unable to consider all data simultaneously. In this work, we propose an analysis method applicable to the massive measurement sets based on a machine learning technique, called autoencoder (AE). The performance of the original AE model has been boosted in this work thanks to its regularization, by imposing orthogonality of its internal representation and by training only half of the model weights while the second half is deduced. Practically, once the model is trained on the data set of interest, it is used to study relationships between input variables through the chart of the circle of correlations. In this chart, input variables are projected as vectors into the compression plane where the angular distance between them will express their degree of correlation. The representation of massive measurements data through a condensed simple chart that still shows complex interactions between variables is in fact very efficient and facilitating the interpretation of measurements themselves. The experimental validation of the proposed method has been done with measurements of Contact Hole (CH) patterns acquired on samples manufactured with 3 different exposure conditions (underexposure dose, nominal exposure dose, and overexposure dose). The proposed data analysis technique allowed us to clearly identify the impact of the process conditions on patterns characteristics, such as their eccentricity, their geometry, and more. Our results indicated that in case of overexposure, an anisotropic distortion of the CH geometry is present, where gamma-axis is the major axis, with larger impact on the pattern surface as compared to the X-axis. This
Dear editor,In the industrial processes, timely detection of key quality variables is very important for tracking the product quality, monitoring the process status, and achieving stable and reliable control. However,...
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Dear editor,In the industrial processes, timely detection of key quality variables is very important for tracking the product quality, monitoring the process status, and achieving stable and reliable control. However, the key quality variables are difficult to measure or have obvious time delay. The process
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