Traditionally, the placement and routing stages of a physical design are performed separately. Because of the additional complexities arising in advanced technology nodes, they have become more interdependent. Therefore, creating efficient cooperation between the routing and placement steps has become an important topic in Electronic Design Automation (EDA). In this article, a framework that allows cooperation between routing and placement is proposed. The main objective of the proposed framework is to improve the detailed routing solution by combining routing and placement. The core of this framework is the Cooperation between Routing and Placement (CRP2.0) 1 engine including techniques to combine routing and placement. The key contributions of CRP2.0 include an Integer Linear Programming (ILP)-based Detailed Placement (ILP-DP), net classification, and two Cost and Net Caching techniques. The efficacy of the proposed framework is evaluated on the official ACM/IEEE International Symposium on Physical Design (ISPD) 2018 and 2019 contest benchmarks. In this article, we show that by using the Cost Caching technique, the global routing runtime compared with state-of-the-art algorithms was reduced by 28.56%, on average. Moreover, numerical results show that when working with advanced technology nodes, the proposed framework can improve the detailed routing score by an average of 0.3% while only moving 0.7% of the cells, on average. The proposed engine can be employed as an add-on to the physical design flow between the global routing and detailed routing steps.

Placement and Routing (P&R) are two main steps of the physical design flow implementation. Traditionally, because of their complexity, these two steps are performed separately. But the implementation of the physical design in advanced technology nodes shows that the performance of these two steps is tied to each other. Therefore, creating efficient co-operation between the routing and placement steps has become a hot topic in Electronic Design Automation (EDA). In this work, to achieve an efficient collaboration between the routing and placement engines, an iterative replacement and rerouting framework facilitated with an Integer Linear Programming (ILP)-based legalizer is proposed and tested on the ACM/IEEE International Symposium on Physical Design (ISPD) 2018 contest's benchmarks. Numerical results show that the proposed framework can improve detailed routing vias and wirelength by 2.06% and 0.14% on average in a reasonable runtime without adding new Design Rule Violations (DRVs). The proposed framework can be considered as an add-on to the physical design flow between global routing and detailed routing.

Detailed routing is one of the most challenging aspects of the physical design process. Many of the violations that occur during the detailed routing stage stem from the placement of the cells. In this paper, we propose a deep learning framework to identify short violations that can occur during detailed routing from a placed netlist. One of the advantages of our technique is that by using the proposed deep learning-based predictor, global routing is no longer required as frequently and hence the total runtime for place and route can be significantly reduced. In this paper, we discuss the proposed framework and the methodology for analyzing the extracted features. The experimental results show that the average sensitivity, specificity, and accuracy of Eh?Predictor is above 90%. In addition, we show that Eh?Predictor is up to 14 times faster than NCTUgr for smaller designs and up to 96 times faster for larger designs.

Detecting and preventing routing violations has become a critical issue in physical design, especially in the early stages. Lack of correlation between global and detailed routing congestion estimations and the long runtime required to frequently consult a global router adds to the problem. In this paper, we propose a machine learning framework to predict detailed routing short violations from a placed netlist. Factors contributing to routing violations are determined and a supervised neural network model is implemented to detect these violations. Experimental results show that the proposed method is able to predict on average 90% of the shorts with only 7% false alarms and considerably reduced computational time.

The placement problem has become more complex and challenging due to a wide variety of complicated constraints imposed by modern process technologies. Some of the most challenging constraints and objectives were highlighted during the most recent ACM/IEEE International Symposium on Physical Design (ISPD) contests. In this article, the framework of Eh?Placer and its developed algorithms are elaborated, with the main focus on modern technology constraints and runtime. The technology constraints considered as part of Eh?Placer are fence region, target density, and detailed routability constraints. We present a complete description on how these constraints are considered in different stages of Eh?Placer. The results obtained from the contests indicate that Eh?Placer is able to efficiently handle modern technology constraints and ranks highly among top academic placement tools.