Using Machine Learning Techniques to Determine DDR5 SDRAM I/O Buffer’s Slew Rate at Different PVT Variations

This study focuses on optimizing specialized input/output (I/O) buffers for DDR5 SDRAMs, specifically examining slew rate variations due to Process, Voltage, and Temperature (PVT) variations. Using advanced techniques like Generalized Regression Neural Network (GRNN) and Genetic Algorithms (GA), the research models slew rate (SR) changes under diverse PVT variations. The dataset taken from simulations has been partitioned into two distinct sections, namely the training set and testing set, with proportions of 80% and 20% respectively. The simulation yields comparative outcomes for the real and anticipated SR. Furthermore, GA exhibits superior performance in forecasting slew rate when compared to GRNN. The regression value yields a significantly closer approximation to unity, thereby achieving a better fit to the dataset. The R2 coefficient of 0.98705 indicates a strong linear relationship between the provided dataset and the line of best fit. Surprisingly, based on the model, a temperature detector circuit was found redundant, resulting in substantial power and area savings. However, voltage variations significantly impacted slew rate. A voltage detector design is therefore recommended. This research advances I/O buffer optimization, offering crucial insights into temperature, voltage, and slew rate dynamics.