Abstract:
The development and evolution of Static Random Access Memory (SRAM) has been a cornerstone in the advancement of semiconductor technology, largely due to its ability to deliver high-speed performance while consuming minimal power. SRAM is widely utilized in applications requiring fast and efficient memory access, such as microprocessors, cache memory, and embedded systems. As the demandfor morepowerful, energy-efficient, and scalable memory solutions grows, understanding the underlying innovations and trends within SRAM technology has become increasingly vital. This project presents a novel approach by combining the power of Large Language Models (LLMs) with a chatbot designed specifically for the analysis of SRAM-related patents. The mainobjective of this project is to harness the capabilities of LLMs to create a tool that can provide comprehensive, context-sensitive, and insightful information from SRAM patents. By systematically classifying and analyzing a wide array of patent data, this system is able to cover key aspects of SRAM technology, such as technical specifications, design innovations, architectural variations, performance optimization techniques, manufacturing advancements, and emerging trends in the field. The rich variety of data available in patent documents presents a unique opportunity to identify cutting-edge developments, breakthroughs, and potential challenges facing the SRAMindustry.
