论文信息 - Pre-Silicon Yield Estimation using Machine Learning Regression

Pre-Silicon Yield Estimation using Machine Learning Regression

Early (pre-silicon) yield estimation with regard to technology variations represents a cost-effective solution in the semiconductor industry. This paper presents preliminary results on a yield estimation methodology that models and uses the Electrical Parameters dependence on the manufacturing process variation, namely the Process Control Monitor parameters.

Georg Pelz | Emilian David | Elena-Diana Şandru

[1] Corneliu Burileanu,et al. Modeling the Dependencies between Circuit and Technology Parameters for Sensitivity Analysis using Machine Learning Techniques , 2019, 2019 16th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD).

[2] Jie Zhang,et al. Artificial Neural Networks in Production Scheduling and Yield Prediction of Semiconductor Wafer Fabrication System , 2016 .

[3] Hyo-Heon Ko,et al. A semiconductor yields prediction using stepwise support vector machine , 2009, 2009 IEEE International Symposium on Assembly and Manufacturing.

[4] Yiorgos Makris,et al. Yield Forecasting Across Semiconductor Fabrication Plants and Design Generations , 2017, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[5] N. L. Johnson,et al. Continuous Multivariate Distributions, Volume 1: Models and Applications , 2019 .

[6] Yiyu Shi,et al. Variability-Aware Parametric Yield Estimation for Analog/Mixed-Signal Circuits: Concepts, Algorithms, and Challenges , 2014, IEEE Design & Test.