Project description DEENESFRITPL Machine learning could accelerate the design of sophisticated analogue ci The era of smart-everything has led to a surge in the need for advanced semiconductor technologies and intelligent data processing. Despite advances in the field, analogue circuit design lags behind its digital counterpart: analogue circuits are still produced in the laboratory, which results in error-prone cycles and high development costs. The EU-funded AnalogCreate project will tap into the potential of machine learning to speed up the design of advanced integrated circuits for promising information and communication applications. Project activities will enable for the first time the autonomous creation of affordable analogue circuits from specifications to fully verified layout – all without being amenable to human feedback. Show the project objective Hide the project objective Objective Progress in semiconductor technology and in intelligent data processing are converging today, opening the door to countless smart ICT applications through the Cloud and Internet of Everything, to the people’s benefit in years to come. Applications that interact with the physical world (e.g. environmental sensing, healthcare, autonomous vehicles, etc.), also need analog integrated circuits in the cyber-physical or edge layer. But while digital circuits are largely synthesized automatically through software, the analog circuits are mainly still handcrafted in industry with low design productivity. This results in long and error-prone design cycles, and the high development costs jeopardize many potential new ICT applications from ever being realized (e.g. solutions for rare diseases). It becomes even more problematic when moving to advanced technologies below 16 nm CMOS, that come with way more design and layout rules to be dealt with. The showstopper for state-of-the-art analog synthesis tools is that they require design heuristics and constraints to be entered explicitly by designers in order to handle the humongous solution space and to steer the circuit and layout optimizations towards acceptable solutions. The proposed disruptively new approach is to use the self-learning capabilities of advanced machine learning algorithms to self-learn and then exploit the design expertise and constraints from the many available successfully completed designs. Also a true circuit topology synthesis approach will be developed to create a proper (possibly novel) schematic from the target specifications, as well as an innovative formal analog design verification approach based on Quick Error Detection. These innovations will enable for the first time ever to truly autonomously create analog circuits from specifications to fully verified layout without direct input from any designer in the loop, and therefore enable the affordable implementation of many promising ICT applications. Fields of science engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehiclesengineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringanalogue electronicsnatural sciencescomputer and information sciencesdata sciencedata processing Keywords analog and mixed-signal integrated circuits design automation autonomous synthesis design creation self-learning Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-2020-ADG - ERC ADVANCED GRANT Call for proposal ERC-2020-ADG See other projects for this call Funding Scheme ERC-ADG - Advanced Grant Host institution KATHOLIEKE UNIVERSITEIT LEUVEN Net EU contribution € 2 500 000,00 Address OUDE MARKT 13 3000 Leuven Belgium See on map Region Vlaams Gewest Prov. Vlaams-Brabant Arr. Leuven Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 2 500 000,00 Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all KATHOLIEKE UNIVERSITEIT LEUVEN Belgium Net EU contribution € 2 500 000,00 Address OUDE MARKT 13 3000 Leuven See on map Region Vlaams Gewest Prov. Vlaams-Brabant Arr. Leuven Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 2 500 000,00
