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Machine Learning for the generation of vias in Integrated Circuits

Rehan Razzaque Rajput

Machine Learning for the generation of vias in Integrated Circuits.

Rel. Andrea Calimera. Politecnico di Torino, Corso di laurea magistrale in Ict For Smart Societies (Ict Per La Società Del Futuro), 2020

Abstract:

Integrated circuits are at the core of the electronic systems. They deliver the functionality of the system within the design envelope for power, performance and form factor. A typical IC consists of multiple layers of metal and polysilicon which are interconnected by electrical connections called vias. In IC fabrication, vias can be generated wherever two or more conductive layers overlap. However, via generation is bound by certain constraints that need not be violated. In today’s competitive market, all the major semiconductor and systems companies need to have unique features in their design that should stand out in the market. For this reason, every company has its own set of constraints. The current heuristic algorithm used for via generation - called AutoVia - is proving to be slow for very large scale designs. The idea of this internship was to develop a machine learning-based approach towards via creation. The results provided by the thesis give a very good starting point for a machine learning-based approach for via generation, as it performed very well on all the metrics against the traditional heuristic approach. If this approach is pursued further, it has the potential to replace the heuristic approach completely in future designs.

Relators: Andrea Calimera
Academic year: 2019/20
Publication type: Electronic
Number of Pages: 82
Additional Information: Tesi secretata. Fulltext non presente
Subjects:
Corso di laurea: Corso di laurea magistrale in Ict For Smart Societies (Ict Per La Società Del Futuro)
Classe di laurea: New organization > Master science > LM-27 - TELECOMMUNICATIONS ENGINEERING
Ente in cotutela: TELECOM ParisTech - EURECOM (FRANCIA)
Aziende collaboratrici: Cadence Design Systems
URI: http://webthesis.biblio.polito.it/id/eprint/14378
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