Detailed routing violation prediction during placement using machine learning
Detailed routing violation prediction during placement using machine learning
复制标题
使用机器学习在贴装过程中进行详细的布线违规预测
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
L. Behjat
中科院分区：
文献类型：
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作者：
Aysa Fakheri Tabrizi;Nima Karimpour Darav;L. Rakai;A. Kennings;L. Behjat
22nm及以下设计规则的复杂性排除了将详细路由（DR）规则直接合并到放置算法中的可能性。然而，在放置过程中忽略路由可达性规则可能导致设计不可行。由全局路由器估计的拥塞通常用于放置期间的路由估计，但它不包括真正详细的路由违规，这对设计的可达性产生不利影响。目前，还没有一种方法能够直接预测详细的路由冲突。在本文中，我们提出了一种基于机器学习的方法来预测短路，这是详细路由违规的主要组成部分。所提出的方法可以集成到放置工具中，并在放置过程中作为指导，以减少在详细布线阶段发生的短路次数。实证结果表明，该方法对无空头违规区域的空头预测成功率为88%，错误预测率仅为16%。
The complexity of design rules at 22nm and below precludes direct incorporation of detailed routing (DR) rules into a placement algorithm. However, ignoring routability rules during the placement process may result in infeasible designs. The congestion estimated by a global router is conventionally used for routing estimation during placement, but it does not include real detailed routing violations, which adversely affect the routability of a design. Presently, there are no methods that directly aim to predict detailed routing violations. In this paper we propose a machine learning based method to predict the shorts that are a major component of detailed routing violations. The proposed method can be integrated into a placement tool and be used as a guide during the placement process to reduce the number of shorts happening in the detailed routing stage. Empirical results show that our method is successful in predicting 88% of the shorts with only 16% incorrectly predicting shorts in no short violation area.