For the competitiveness of the European economy, automation techniques in the design of complex electronic systems are a prerequisite for winning the global chip challenge. Specifically, while the physical design of digital Integrated Circuits (ICs) can be largely automated, the physical design of Analog Mixed-Signal (AMS) ICs built with an analog-on-top flow, where digital subsystems are instantiated as Intellectual Property (IP) modules, is still carried out predominantly by hand, with a time-consuming methodology. The AMBEATion consortium, including global semiconductor and design automation companies as well as leading universities, aims to address this challenge by combining classic Electronic Design Automation (EDA) algorithms with novel Artificial Intelligence and Machine Learning (ML) techniques. Specifically, the scientific and technical result expected at the end of the project will be a new methodology, implemented in a framework of scripts for AMS placement, internally making use of state-of-the-art AI/ML models, and fully integrated with Industrial design flows. With this methodology, the AMBEATion consortium aims to reduce the design turnaround-time and, consequently, the silicon development costs of complex AMS ICs.

AMBEATion: Analog Mixed-Signal Back-End Design Automation with Machine Learning and Artificial Intelligence Techniques

Aliffi, Giulia Elena;
2024-01-01

Abstract

For the competitiveness of the European economy, automation techniques in the design of complex electronic systems are a prerequisite for winning the global chip challenge. Specifically, while the physical design of digital Integrated Circuits (ICs) can be largely automated, the physical design of Analog Mixed-Signal (AMS) ICs built with an analog-on-top flow, where digital subsystems are instantiated as Intellectual Property (IP) modules, is still carried out predominantly by hand, with a time-consuming methodology. The AMBEATion consortium, including global semiconductor and design automation companies as well as leading universities, aims to address this challenge by combining classic Electronic Design Automation (EDA) algorithms with novel Artificial Intelligence and Machine Learning (ML) techniques. Specifically, the scientific and technical result expected at the end of the project will be a new methodology, implemented in a framework of scripts for AMS placement, internally making use of state-of-the-art AI/ML models, and fully integrated with Industrial design flows. With this methodology, the AMBEATion consortium aims to reduce the design turnaround-time and, consequently, the silicon development costs of complex AMS ICs.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3340052
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