Design-Technology Interaction From Data-Mining Perspective
Choongyeun Cho, Daeik Kim, Jonghae Kim, and Jean-Olivier Plouchart
IBM Academy of Technology Conference on Analog Design, Technology, Models, and Tools 2008, May 2008
Abstract
A data-mining approach is presented to tackle key issues in design-technology interaction: What is relationship between a set of M1 test data from typical technology benchmarks (e.g. FETs and ring oscillators) and analog circuit performance/yield? And, how do we best exploit it? How does process variation affects analog product performance?; The statistical data-driven approach identifies device characteristics that are most correlated with a product performance, and estimates performance yield. A statistical method that isolates systematic process variations on die-to-die and wafer-to-wafer levels is also presented. The proposed framework enables translations of interactions among technology, product, and model, and facilitates collaborative efforts accordingly. I will also present a practical method to estimate analog product performance and yield solely from a set of existing M1 electrical measurements intended for technology benchmark.; The proposed methodology is applied to first three development generations of 65nm SOI technology node and microprocessor product current-controlled oscillators (ICOs) for phase-locked loops (PLLs) that were migrated from 90nm. Automated manufacturing floor in-line characterization and bench RF measurements are used for the methodology. The ICO exhibits yield improvement of RF oscillation frequency from 47% to 99% across three different 65nm SOI technology generations.
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