Design and Optimization of Ddr6 Sram Using Machine Learning Techniques

Abstract

Memory design plays a crucial role in modern digital systems, influencing performance, power efficiency, and storage capacity. As computing applications demand higher data processing speeds and larger storage capabilities, advancements in semiconductor memory technologies have become essential. The work focuses on the scaling of a 1-bit SRAM to 8 Gb using machine learning techniques, optimizing power consumption, access time, and area efficiency while ensuring reliability and performance. A comparative analysis of CMOS and FinFET technologies is conducted using HSPICE simulations to evaluate their impact on memory efficiency and scaling challenges. In parallel, this research investigates Double Data Rate (DDR) memory, a widely used Synchronous Dynamic Random-Access Memory (SDRAM) technology that enhances data transfer rates by utilizing both rising and falling edges of the clock signal. A key aspect of this research is the analysis of time slack violations in DDR Burst SRAM. Timing slack, which represents the difference between required and actual signal arrival times, is critical in determining the reliability of high-speed memory designs. Negative slack indicates timing violations that can lead to data integrity issues and system failures. This work employs static timing analysis (STA) techniques and machine learning-based prediction models to determine whether timing slack violations occur in DDRB SRAM, addressing potential solutions through circuit-level and architectural optimizations. The findings provide insights into efficient SRAM scaling strategies and emphasize the importance of timing analysis in DDR memory designs, ensuring the future stability and efficiency of high-performance computing systems.