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Abstract-A novel technique is presented for the accurate, rapid, high frequency, predictive modeling of parallel plate capacitors with gridded plates manufactured in a multilayer low temperature cofired ceramic (LTCC) process. The method is empirical in nature and is based on the concept of incrementally constructing the model for a structure from well characterized individual building blocks. Building blocks are characterized by the use of test structures and measurements, and are modeled using passive lumped circuit elements. This method is applied to the predictive modeling of deeply embedded gridded parallel plate capacitor structures. The procedure has been experimentally verified, with accurate predictions of behavior obtained up to the second self resonance for large area gridded parallel plate capacitors. Since lumped element circuits are generated by this method, structure prediction speed is determined by circuit size and simulator small signal analysis time. The method is versatile and is well suited for circuit design applications.

This paper presents a novel procedure for predicting integrated circuit parametric performance and yield when provided with sample transistor test results and a circuit schematic. Two enhancements to the existing Monte Carlo simulation procedures are described: 1) a multivariate nested model is used to reproduce random process-induced device variations, rather than the multivariate multinormal model typically used, and 2) the stochastic Monte Carlo method for mapping process variability into a performance distribution is replaced with a deterministic mapping technique. The use of multivariate nested distributions allows estimation not only of correlation between various model parameters, but also allows each of those variations to be apportioned among the various stages of the process (i.e., wafer to wafer, lot to lot, etc.). This allows matched devices to be more accurately simulated, without having to develop customized models for each configuration of matching, and provides focus for process improvement efforts into those areas with the maximum potential reward. The use of deterministic mapping provides simulation results which are repeatable and do not rely on chance to insure that the process parameter space has been evenly explored. A software package which implements the entire procedure has been written in C++.

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