Leveraging AI-Driven Approach for Innovating IO Characterization

Abstract

Standard cell modelling requires library characterization as an essential design procedure which generates precise and dependable models that detail their timing performance in addition to their power consumption and other functional parameters. System-on-Chip (SoC) implementation requires this process to make sure digital chip designs achieve specified performance power area (PPA) targets successfully. The characterization technique operated by Siemens' Kronos depends on Eldo analog simulations, but this method leads to long development cycles and high computational resource utilization. The established techniques experience reduced accuracy effectiveness when used for multiple electrical specifications while the industry moves towards manufacturing denser designs. Siemens introduced Solido Characterization Suite through advanced machine learning-based algorithms to optimize characterization processes. The innovative suite decreases the requirement for large-scale simulations to cut down on computational expenses and reduce characterization periods. The Solido Generator stands among the principal elements of the suite by quickly analysing design characteristics across selected Process, Voltage and Temperature (PVT) regions to establish new PVT data points with high precision. The report analyses problems with conventional library characterization approaches then describes how Solido Characterization Suite offers advanced solutions to these issues. The report uses detailed examinations and specific case examples to show how machine learning transforms characterization processing into a quick and correct method of library development. The Solido Characterization Suite emerges as a fundamental tool in present-day semiconductor design because it shows great potential for enhancing design efficiency together with reliability improvements.