Accelerated Channel Operating Margin for Automated Context and Applications to Design Optimization

Rapidly becoming a cornerstone of signal integrity metrics, Channel Operating Margin (COM) provides a highly desirable single figure of merit encapsulating performance guarantees and providing a variety of byproducts yielding insight on channel behavior. This poigniant metric has been anticipated as an effective tool for automated design problems from root cause analysis to design optimization. However, in practice it has largely been mired in the manual design regime, requiring a great depth of tooling and expert input to assess efficiently. The most substantial such bottleneck derives from the dual design problem of finding optimal equalizer settings for general transmitters and receivers given a channel description, which has historically been approached with an expert-guided grid search. We tackle this issue by introducing a practical method in Bayesian optimization accelerated by a lightweight transfer learning framework. We additionally present our methods in developing an unsupervised flow including derivation of channel descriptions using automated PowerSI tooling, fast time-domain assessment, and flexible frequency-space channel simulation. Finally, we discuss briefly the state of applications to design optimization. In sum, we develop a method for automated COM analysis from design to metric permitting the rapid analysis of entire PCBs.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Gromko, Zackary

Advisors dc:contributor.advisor

• Daniel, Luca
• Farrahi, Shirin

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/145070

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/145070