Application of artificial neural network for efficient hardware characterization of high-speed interconnect systems

The impact of process variations, operating conditions and environmental changes on high-speed system performances is becoming more significant due to the shrinking bit time and supply voltage. Consequently, accurate and exhaustive characterization is essential to improve the robustness and yield of current systems. However, the statistics of the system performance obtained from limited measurement can be unrepresentative of the final product. This paper describes the application of an artificial neural network (ANN) to generate the performance distribution and statistics of chip-to-chip communication system from measurements. A set of experiments is performed on a hardware system to capture the relationships between system parameters and performance. The measured data is then used to train the neural network that is used to efficiently generate realistic distributions and statistics of the system performance. The accuracy of the proposed approach is verified using a prototype system. The sensitivity of setup and hold times of the system is studied as functions of supply voltages and temperature. The results of the proposed method are also compared to those from measurements and regression models.