论文标题
使用基于CNN的功能提取器进行设计规则检查
Design Rule Checking with a CNN Based Feature Extractor
论文作者
论文摘要
在高级节点技术中，设计规则检查（DRC）变得越来越复杂。非常需要在布局过程中使用可以使用的快速交互式DRC引擎。在这项工作中，我们建立了这种引擎可行性的证明。所提出的模型由训练有素的卷积神经网络（CNN）组成。该模型接受了人造数据培训，这些数据是从一组$ 50 $ SRAM设计中得出的。该演示的重点是金属1规则。使用此解决方案，我们可以比布尔检查器快32倍检测到多达92的布尔检查器。提议的解决方案可以很容易地扩展到完整的规则集。
Design rule checking (DRC) is getting increasingly complex in advanced nodes technologies. It would be highly desirable to have a fast interactive DRC engine that could be used during layout. In this work, we establish the proof of feasibility for such an engine. The proposed model consists of a convolutional neural network (CNN) trained to detect DRC violations. The model was trained with artificial data that was derived from a set of $50$ SRAM designs. The focus in this demonstration was metal 1 rules. Using this solution, we can detect multiple DRC violations 32x faster than Boolean checkers with an accuracy of up to 92. The proposed solution can be easily expanded to a complete rule set.
