Use of machine learning in radio frequency integrated circuits (RFIC) development

This Master's Thesis starts with an introduction to the radio frequency integrated circuits (RFICs) industry and a discussion on the key problem of the existing RFIC development process: the need for multiple trial and error iterations due to inaccurate simulations. This simulation inaccuracy happens because the existing electronic design automation (EDA) software, and the underlying physics-based IC models, fail to fully capture the nonlinear, frequency-dependent RF parasitic effects. To overcome this problem, in this thesis we propose the use of machine learning in RFIC development. Machine learning uses statistical models to recognize hidden patterns from sample data points, known as "training"; generalize patterns; and make predictions based on new data. In theory, machine learning can capture the nonlinear, frequency-dependent RF parasitic effects very well thanks to the large variety of nonlinear modelling techniques at its disposal, such as polynomial regressions and neural networks. Therefore, this thesis investigates for the first time the feasibility of using machine learning in RFIC development to solve the problem of inaccurate RFIC simulation.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering Systems Division

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Cui, Qiang (Computer engineer).

Advisor dc:contributor.advisor

• Tomas Palacios.

• Engineering Systems Division.
• System Design and Management Program.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/145240

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/145240