(untitled) (International Journal of Food and Nutritional Sciences, 2022)
The integration of Artificial Intelligence and Machine Learning techniques into the domain of Very Large Scale Integration has ushered in a new era of possibilities for enhancing the efficiency of analog layout. This research investigates into the utilization of OpenCV, a computer vision library, in conjunction with AI and ML algorithms, to advance the design methodology and the quality of analog layout design. This design process is implemented and verified on phase locked loop circuit using 45nm technology node. By scrutinizing layout images and discerning areas of underutilized space, OpenCV emerges as a powerful tool, guiding designers towards the optimization of spatial resources. The result of this work assured creation of smaller, more efficient layouts that harness the potential of AI and ML tools, ultimately streamlining the design process and elevating the performance of integrated circuits (ICs). The proposed methodology applied on PLL circuits using Python based color identification algorithm results unused layout area of 77.5%.