Extending ML-OARSMT to Net Open Locator with Efficient and Effective Boolean Operations

• Main Authors: Jiang, Bing-Hui, 江炳輝
• Other Authors: Chen, Hung-Ming
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/rhdfyn

碩士 === 國立交通大學 === 電子研究所 === 107 === Multi-layer obstacle-avoiding rectilinear Steiner minimal tree (ML-OARSMT) problem has been extensively studied in recent years. In this work, we consider a variant of ML- OARSMT problem and extend the applicability to the net open location finder. Since ECO or router limitations may cause the open nets, we come up with a framework to detect and reconnect existing nets to resolve the net opens. Different from prior connection graph based approach, we propose a technique by applying efficient Boolean operations to repair net opens. Our method has good quality and scalability and is highly parallelizable. Compared with the results of ICCAD-2017 contest, we show that our proposed algorithm can achieve the smallest cost with 4.81 speedup in average than the top-3 winners.