# Submission #8

Yes

Industry

Probabilistic methods for risk evaluation in industrial production

PhD Thesis

STMicroelectronics

http://www.st.com

philippe.leduc@st.com

Tours

France

Theme of research :

In competitive industries, a reliable yield forecasting is a prime factor to accurately determine the production costs and therefore ensure profitability. Indeed, quantifying the risks long before the effective manufacturing process enables fact-based decision-making. From the development stage, improvement efforts can be early identified and prioritized.

The complex relationship between the process technology and the product design (non linearities, multi-modal features...) is handled via random process regression. A random field (Gaussian, Student…) encodes, for each product configuration, the available information regarding the risk of noncompliance.

The probabilistic nature of the model is then operated to derive a failure risk probability, defined as a random variable. To do this, our approach is to consider as random all unknown, inaccessible or fluctuating data. In order to propagate uncertainties, a fuzzy set approach provides an appropriate framework for the implementation of a Bayesian model mimicking expert elicitation. The underlying leitmotiv is to insert minimal a priori information in the failure risk model.

Keywords : Kriging, Bayesian inference, Monte-Carlo simulation, uncertainty analysis, fuzzy set, decision theory, manufacturing yield evaluation

Job description :

Theoretical study and practical implementation of a probabilistic method in order to evaluate the failure probability from a set of experiments (real or virtual) providing partial information on the device or circuit under study. Data representation using interpolation models rigourously justified.

In competitive industries, a reliable yield forecasting is a prime factor to accurately determine the production costs and therefore ensure profitability. Indeed, quantifying the risks long before the effective manufacturing process enables fact-based decision-making. From the development stage, improvement efforts can be early identified and prioritized.

The complex relationship between the process technology and the product design (non linearities, multi-modal features...) is handled via random process regression. A random field (Gaussian, Student…) encodes, for each product configuration, the available information regarding the risk of noncompliance.

The probabilistic nature of the model is then operated to derive a failure risk probability, defined as a random variable. To do this, our approach is to consider as random all unknown, inaccessible or fluctuating data. In order to propagate uncertainties, a fuzzy set approach provides an appropriate framework for the implementation of a Bayesian model mimicking expert elicitation. The underlying leitmotiv is to insert minimal a priori information in the failure risk model.

Keywords : Kriging, Bayesian inference, Monte-Carlo simulation, uncertainty analysis, fuzzy set, decision theory, manufacturing yield evaluation

Job description :

Theoretical study and practical implementation of a probabilistic method in order to evaluate the failure probability from a set of experiments (real or virtual) providing partial information on the device or circuit under study. Data representation using interpolation models rigourously justified.

25000-30000

Euro

Tue, 09/01/2015