Routability-aware Placement Guidance Generation for Mixed-size Designs

Chieh-Yu Cheng and Ting-Chi Wang
National Tsing Hua University


Placement is a critical step in a modern physical design flow, and the routability of the placement result is a major issue that must be taken into account. In this work, we explore the possibility of using placement guidance to mitigate routing congestion and reduce design rule violations for mixed-size designs. By extracting the underlying knowledge of a mixed-size design using a graph neural network, we generate an embedding for each standard cell. Based on the embeddings, we cluster standard cells into groups and create the placement guidance. By adding the placement guidance to a commercial place-and-route tool, the tool will strive to avoid the fragmentation of standard cells with dense connections in the placement stage. Experimental results show that our placement guidance generation methodology helps the commercial tool reduce 26% routing overflow and 65% design rule violations for the test cases.