Reliability-Aware Statistical BSIM Compact Model Parameter Generation Methodology

This article presents an accurate, reliability-aware statistical Berkeley short-channel IGFET Model 4 (BSIM4) compact model parameter generation methodology using the generalized lambda distribution (GLD) method. Using this methodology, compact model parameter sets can be generated 'on the fly' beyond the limitation of the number of compact models extracted from the TCAD simulation. The generated unlimited parameter sets enable circuit designer for the statistical circuit simulation. An analytical model has been developed to interpolate TCAD simulation data points, which enables statistical compact model parameter sets to be generated at any aging level. The capability to generate such intermediate aging model parameters at trap densities that were not physically simulated has important application in statistical circuit simulation, opening up the possibility to include accurately reliability assessment in circuit design. An aging model that can transfer trap density to stress/aging time is an integral part of the presented methodology. The accuracy of the compact model parameter generation methodology is validated by comparing the new generated compact model parameter sets at an interpolated trap density, against physical 'atomistic' 3-D TCAD simulation. The compact model parameter generation methodology enables the accurate investigation of the influence of statistical variability and bias temperature instability (BTI)-induced aging at circuit level.