Predictive Analytics in Machine Learning for VLSI Circuits

Abstract

As semiconductor technology enters the sub-14nm era, geometry, process, voltage and temperature (PVT) variability in devices can affect the performance, functionality, and power of circuits, especially in new Artificial Intelligent (AI) accelerators. This is where predictive failure analytics is extremely critical. It can identify the failure issues related to logic and memory circuits and drive the circuits in energy efficient area.

This talk describes how key statistical techniques and new algorithms can be effectively used to analyze and build robust circuits.  These algorithms can be used to analyze decoders, latches, and volatile as well as non-volatile memories. In addition, how these methodologies can be extended to “reliability prediction” and “hardware corroboration” is demonstrated. Logistic regression-based machine learning techniques are employed for modeling the circuit response and speeding up the importance sample points simulations. To avoid overfitting, a cross-validation based regularization framework for ordered feature selection is demonstrated.  

Also, techniques to generate accurate parasitic capacitance modeling along with PVT variations for sub-22nm technologies and their incorporation into a physics-based statistical analysis methodology for accurate V_min analysis are described. In addition, extension of these techniques based on machine learning e.g KNN is highlighted. Finally, the talk summarizes important issues in this field.