Post-Silicon system margin validation consumes a significant amount of time and resources. To overcome this, a reduced validation plan for derivative products has previously been used. However, a certain amount of validation is still needed to avoid escapes, which is prone to subjective bias by the validation engineer comparing a reduced set of derivative validation data against the base product data. Machine Learning techniques allow, to perform automatic decisions and predictions based on already available historical data. In this work, we present an efficient methodology implemented with Machine Learning to make an automatic risk assessment decision and eye margin estimation measurements for derivative products, considering a large set of parameters obtained from the base product. The proposed methodology yields a high performance on the risk assessment decision and the estimation by regression, which translates into a significant reduction in time, effort, and resources.
