Modeling and design of on-chip interconnect, the interconnection between the components is becoming the fundamental roadblock in achieving high-speed integrated circuits. The scaling of interconnect in nanometer regime had shifted the paradime from device-dominated to interconnect-dominated design methodology. Driven by the expanding complexity of on-chip interconnects, a passivity preserving model order reduction (MOR) is essential for designing and estimating the performance for reliable operation of the integrated circuit. In this work, we developed a new frequency selective reduce norm spectral zero (RNSZ) projection method, which dynamically selects interpolation points using spectral zeros of the system. The proposed reduce-norm scheme can guarantee stability and passivity, while creating the reduced models, which are fairly accurate across selected narrow range of frequencies. The reduced order results indicate preservation of passivity and greater accuracy than the other model order reduction methods.

The continuous scaling of the on-chip devices and interconnects increases the complexity of the design space and becomes a crucial factor in the fabrication of modern integrated circuits. The ever decreasing of interconnect pitch along with process enhancement into the nanometer regime had shifted the paradigm from a device-dominated to an interconnect-dominated methodology. In the design methodology, Model Order Reduction (MOR) reduces the size of large-scale simulation of on-chip interconnect to speed up the performance of design tools and chip validation. In approximating the original system, the passivity preserving MOR technique of using spectral zeros as positive real interpolation points preserves the stability and passivity of the system. In this work, statistical distribution techniques are proposed for the selection of spectral zeros. The proposed method is based on using the gaussian, uniform, binomial, and weibull distributions to select spectral zeros to better match moments with the least absolute error between the original and reduced-order systems. The results show that the reduced-order model developed using the Gaussian distributed Spectral zeros Projection (GSP) method offers higher accuracy and numerical stability compared to other distributions.