Abstract--Accurate estimation of voltage drop (IR drop) in modern Application-Specific Integrated Circuits (ASICs) is highly time and resource demanding, due to the growing complexity and the transistor density in recent technology nodes. To mitigate this challenge, we investigate how Machine Learning (ML) techniques, including Extreme Gradient Boosting (XGBoost), Convolutional Neural Network (CNN), and Graph Neural Network (GNN) can aid in reducing the computational effort and implicitly the time required to estimate the IR drop in Integrated Circuits (ICs). ML algorithms, on the other hand, are explored as an alternative solution to offer quick and precise IR drop estimation, but in considerably less time. This study illustrates the effectiveness of ML algorithms in precisely estimating IR drop and optimizing ASIC sign-off. Thus, a new round of simulations is required for verification. This process is a standard routine in every ASIC design and manufacturing process, and it is defined as the "sign-off" REDICTION of IR drop is an important problem faced today often by ASIC designers. As the current (I) flows With the transition to larger density integration of transistors, through the Power Distribution Network (PDN), a part of the number of connection layers and interconnections the applied voltage inherently drops across the current path, have increased exponentially over the last decades, driven which is, in simple terms, the definition of IR drop. As a result, while commercial results in voltage drop, or to the grounding (GND), which tools are trying to keep up with the up-scaling demand, results in a ground bounce.

Cellular coverage quality estimation has been a critical task for self-organized networks. In real-world scenarios, deep-learning-powered coverage quality estimation methods cannot scale up to large areas due to little ground truth can be provided during network design & optimization. In addition they fall short in produce expressive embeddings to adequately capture the variations of the cells' configurations. To deal with this challenge, we formulate the task in a graph representation and so that we can apply state-of-the-art graph neural networks, that show exemplary performance. We propose a novel training framework that can both produce quality cell configuration embeddings for estimating multiple KPIs, while we show it is capable of generalising to large (area-wide) scenarios given very few labeled cells. We show that our framework yields comparable accuracy with models that have been trained using massively labeled samples.