Large Scale Integration

Large-scale integration (LSI) in chip design faces increasing complexity due to shrinking transistor sizes and rising component counts. Current research focuses on applying machine learning, particularly neural networks like U-Nets and reinforcement learning, to optimize various stages of the design process, including placement, routing, and partitioning for multi-chip modules (MCMs). These AI-driven approaches aim to improve efficiency, reduce design time, and enhance the yield of integrated circuits, addressing critical challenges in modern VLSI design and manufacturing. The development of comprehensive benchmark datasets, like EDALearn, is crucial for fostering reproducible and transferable ML models across different technologies and design scales.