Automatic Tuning Algorithms and Statistical Circuit Design

In this dissertation two topics are investigated the first of which is automatic tuning algorithms for active filters. Here the problem is that in order to meet response specifications these filters usually must be tuned or adjusted, preferably by computer automation if the production level is high. Three generalized tuning algorithms which have recently appeared in the literature are comparatively reviewed on the basis of their architecture, computational complexity, and effectiveness. Furthermore, a method is presented for the tuning resistor and frequency selection problem, a problem relevant to all three methods. Several statistical simulation examples enhance the presentation. The second topic is statistical circuit design where the emphasis is on Monte Carlo techniques for yield estimation and yield maximization. Several techniques for achieving variance reduction in the yield estimates are discussed. A quadratic approximation model is set up for the circuit and is used to provide an extrapolated yield approximation technique which is extremely effective and efficient in approximating and maximizing the yield along a search direction. Several examples demonstrate the yield maximization process.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Hocevar, Dale Edward

• Engineering, Electronics and Electrical

Identifier

(UMI)AAI8302881

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/69238