Nonlinear Statistical Modeling and Yield Estimation Technique for Use in Monte Carlo Simulations

Abstract

A novel nonlinear statistical modeling technique for microwave devices and a new approach to yield estimation for mi- crowave integrated circuits are presented. The statistical modeling methodology is based on a combination of applied multivariate methods with heuristic techniques. These include principal com- ponent analysis and factor analysis in conjunction with maximally flat quadratic interpolation and group method of data handling. The proposed modeling approach, when applied to the database of extracted equivalent circuit parameters (ECPs) for a pseudo- morphic high electron mobility transistor device, has proven that it can generate simulated ECPs, -parameters, that are statisti- cally indistinguishable from a measured ones. A new yield estima- tion technique based on a Latin hypercube sampling (LHS) is also demonstrated. The LHS-based simulation is utilized as an alterna- tive to primitive Monte Carlo (PMC) simulation in yield analysis. An equally confident yield estimate based on the LHS method re- quires only one-fourth of those simulations needed when the PMC technique is used.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 37 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.