Routing-free crosstalk prediction

Liang, Rongjian; Xie, Zhiyao; Jung, Jinwook; Chauha, Vishnavi; Chen, Yiran; Hu, Jiang; Xiang, Hua; Nam, Gi-Joon

doi:10.1145/3400302.3415712

Cited by 25 publications

(3 citation statements)

References 32 publications

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“…In ref , the author proposes an ML-based framework for routing-free crosstalk prediction. The model identifies nets with considerable crosstalk noise and coupling capacitance.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Application for Output Capacitance Accuracy Improvement: A Case Study for Combinational Cells

Attaoui¹,

Chentouf²,

Ismaili

et al. 2023

ACS Appl. Electron. Mater.

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The lack of physical information at the early gate-level Netlist makes timing accuracy a significant challenge. Advancing in the physical design flow reduces the timing inaccuracy gap in the cell’s delay estimation, where the physical layout information is no longer estimated. This paper aims to improve the cell delay accuracy of Oasys-RTL Synthesis by predicting the cell’s output capacitance using Machine Learning algorithms. We trained and tested various ML Regression algorithms to predict the Oasys-RTL cell’s output capacitance using several 16 nm designs. With the help of a state-of-art tool, APRISA P &R by Siemens EDA, we derived accurate output capacitance values and compared the inaccuracy and error margins between postplaced APRISA and postplaced Oasys-RTL Netlists before and after applying ML models. We reached the highest accuracy using the Random Forest model. The RF model has achieved a total average accuracy of 93.04% and has reduced the RMSE from 2.537 to 0.655.

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“…In ref , the author proposes an ML-based framework for routing-free crosstalk prediction. The model identifies nets with considerable crosstalk noise and coupling capacitance.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Application for Output Capacitance Accuracy Improvement: A Case Study for Combinational Cells

Attaoui¹,

Chentouf²,

Ismaili

et al. 2023

ACS Appl. Electron. Mater.

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“…This method uses a neural network to estimate IR drop, which can significantly reduce the time required for estimation and can provide accurate results even with limited data, making it suitable for early-stage design. Fast estimation of a number of critical chip design metrics, such as design rule violation [10], power [11], crosstalk [12], lithography hotspots [13], testability [14], clock tree's quality [15], placement solution [16], IR drop [17,18], timing and routing solution [19,20] has become possible. There are numerous ML-based IR drop estimators that target different design stages with different emphases.…”

Section: Introductionmentioning

confidence: 99%

Analysis of IR Drop for Robust Power Grid of Semiconductor Chip Design: A Review

Fatima

Chandel

2023

ITM Web Conf.

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Modern semiconductor industry has a major focus on die size reduction and favours the usage of multiple metal layers to increase gross margins. High congestion and voltage or IR drop challenges result from designs with higher and lower core utilisations. Owing to this issue, one of the major difficulties in large silicon-on-chip (SoC) design is the implementation of a power grid design. It’s essential to correctly analyze IR drops to guarantee the reliability of the power grid. This paper presents a comprehensive review approach for the analysis of IR drop for robust power grid design in semiconductor chips. The necessity of the vector-based dynamic analysis and the drawbacks of the vector-less analysis have been addressed. The various techniques used to mitigate IR drop effects, including power grid modeling techniques, use of decoupling capacitors, and voltage drop analysis are explored. The review concludes by identifying the most promising techniques for robust power grid design in semiconductor chips and providing recommendations for future research. The simulations are carried out using ANSYS RedHawk, and the analysis findings are achieved using FinFET technology.

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“…In recent years, machine learning (ML) applications in electronics design automation (EDA) have started to attract wide attention. They have enabled fast estimation on many important metrics for chip design, including timing [2], power [16,42], design rule violation [20,34,39], crosstalk [21], testability [26], lithography hotspots [37,40], clock tree's quality [25], placement solution [27], routing solution [43], and IR drop [10,12,15,23,28,30,35,36]. There have been many ML-based IR drop estimators targeting at various design stages with different emphasis.…”

Section: Introductionmentioning

confidence: 99%

Fast IR drop estimation with machine learning

Xie¹,

Li²,

et al. 2020

Proceedings of the 39th International Conference on Computer-Aided Design

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IR drop constraint is a fundamental requirement enforced in almost all chip designs. However, its evaluation takes a long time, and mitigation techniques for fixing violations may require numerous iterations. As such, fast and accurate IR drop prediction becomes critical for reducing design turnaround time. Recently, machine learning (ML) techniques have been actively studied for fast IR drop estimation due to their promise and success in many fields. These studies target at various design stages with different emphasis, and accordingly, different ML algorithms are adopted and customized. This paper provides a review to the latest progress in ML-based IR drop estimation techniques. It also serves as a vehicle for discussing some general challenges faced by ML applications in electronics design automation (EDA), and demonstrating how to integrate ML models with conventional techniques for the better efficiency of EDA tools.

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Routing-free crosstalk prediction

Cited by 25 publications

References 32 publications

Machine Learning Application for Output Capacitance Accuracy Improvement: A Case Study for Combinational Cells

Machine Learning Application for Output Capacitance Accuracy Improvement: A Case Study for Combinational Cells

Analysis of IR Drop for Robust Power Grid of Semiconductor Chip Design: A Review

Fast IR drop estimation with machine learning

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