Improving Electronic Sensor Reliability by Robust Outlier Screening

Moreno-Lizaranzu, Manuel J.; Cuesta, Federico

doi:10.3390/s131013521

Cited by 32 publications

(8 citation statements)

References 24 publications

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“…Unit level test results collected from a given population, say a wafer or wafer lot, can be statistically analyzed to identify chips that behave differently. For automotive products several algorithms are available to post process test data [12][13] [14] including spatial screens (Z Yield, cluster detection, good die cluster, kernel-based clustering) and distribution-based screens (Static Pat, Regression and Multi Variate Pat, Statistical Bin Limits and Below Minimum Yield) (Figure 10).…”

Section: Advanced Statistical Screeningmentioning

confidence: 99%

A holistic approach to zero defect products when wafer fab does not output zero defects

Traynor¹,

Yanez²

2023

DTCO and Computational Patterning II

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The growth of applications in the automotive space including safety, security, driving assist, vehicle dynamics, networking, etc. is driving exponential demand for superior quality semiconductor products in the vehicle. This carries a serious responsibility for suppliers, as automakers expect quality that can be measured on a defective parts per billion scale (DPPB). Orthogonal to this is the continued aggressively shrinking technology roadmap supporting the new advanced finFET nodes, where exponentially complex processing is creating limitations in Design for Manufacturing needed to achieve Zero Defect products. Additionally, increased product complexity and higher number of product features can lead to lower observability requiring new thinking to detect, accelerate, and prevent escape. This paper explains the challenges and new solutions to delivering products at these strict automotive required intrinsic and extrinsic quality levels, all while at an acceptable product cost. Using real world case studies, it highlights advances in innovative test methods deployed to target “time zero” (T0) random and systemic defects, and Design for Stress and Advanced Outlier Detection techniques used to activate, then screen potentially “latent defects” during factory testing. The paper illustrates why there is no single silver bullet that can deliver ZD product quality in advanced nodes, instead it is necessary that industry experts from fields such as process technology, design, product, test and quality teams, collaborate on and co-deliver complementary process, design and test solutions to achieve the ever-increasing demands for quality in the industry.

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Section: Advanced Statistical Screeningmentioning

confidence: 99%

A holistic approach to zero defect products when wafer fab does not output zero defects

Traynor¹,

Yanez²

2023

DTCO and Computational Patterning II

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“…outliers) to address product quality issues, e.g. [1], [2], a minor part utilizes automated methods for production process monitoring and failure detection.…”

Section: Related Workmentioning

confidence: 99%

“…This guarantees to provide realistic simulations of production data. 2 In the following experiment, approaches (A) and (B) are applied as follows: the available dataset is divided into M train = 4000 training samples and M test = 1000 test samples. The training set is used to estimate the PCA parameters of approach (A), as well as to train the auto-encoder for approach (B).…”

Section: B Synthetic Datasetmentioning

confidence: 99%

Feature Extraction From Analog Wafermaps: A Comparison of Classical Image Processing and a Deep Generative Model

Santos

Schrunner

Geiger

et al. 2019

IEEE Trans. Semicond. Manufact.

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Semiconductor manufacturing is a highly innovative branch of industry, where a high degree of automation has already been achieved. For example, devices tested to be outside of their specifications in electrical wafer test are automatically scrapped. In this work, we go one step further and analyse test data of devices still within the limits of the specification, by exploiting the information contained in the analog wafermaps.To that end, we propose two feature extraction approaches with the aim to detect patterns in the wafer test dataset. Such patterns might indicate the onset of critical deviations in the production process. The studied approaches are: (A) classical image processing and restoration techniques in combination with sophisticated feature engineering and (B) a data-driven deep generative model. The two approaches are evaluated on both a synthetic and a real-world dataset. The synthetic dataset has been modelled based on real-world patterns and characteristics. We found both approaches to provide similar overall evaluation metrics. Our in-depth analysis helps to choose one approach over the other depending on data availability as a major aspect, as well as on available computing power and required interpretability of the results.

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“…6,7 These factors not only are unfavorable for the load spectrum editing process but also have a great influence on the analysis results on the final fatigue life. Therefore, the original data need to be processed by abnormal value screening, 8 signal denoising, 9 and low-amplitude load filtering. Costa 10 proposed a truncation anomaly worthy of filtering to process the anomalous signal collected by the sensor.…”

Section: Introductionmentioning

confidence: 99%

Load spectrum for automotive wheels hub based on mixed probability distribution model

Geng

Liu

Yang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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In actual engineering, the actual road test or indoor bench test is usually used to collect the data of the road load of the parts to acquire the fatigue life estimation of the auto parts. This paper proposes a method for load spectrum construction based on the mixed distribution probability model using the data of the road load spectrum collected in the test site. Pau Ta criteria outlier elimination and wavelet signal denoising are applied to analyze the original road load spectrum data. Then the maximum likelihood estimation method is used to estimate the generalized Pareto distribution parameters of all excesses. The Pareto distribution is also employed to extrapolate the load spectrum. Through the characteristic analysis of the load spectrum, the one-dimensional and two-dimensional program load spectrum of the hub is established based on the mixed probability distribution model, which provides a theoretical basis for the life prediction of the hub. In addition, the research results of this paper provide inspirations for the fatigue life prediction and fatigue durability bench test of automotive parts subjected to the complexity and variability of random loads.

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Improving Electronic Sensor Reliability by Robust Outlier Screening

Cited by 32 publications

References 24 publications

A holistic approach to zero defect products when wafer fab does not output zero defects

A holistic approach to zero defect products when wafer fab does not output zero defects

Feature Extraction From Analog Wafermaps: A Comparison of Classical Image Processing and a Deep Generative Model

Load spectrum for automotive wheels hub based on mixed probability distribution model

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