The various devices (transistors, resistors, etc.) in an integrated
semiconductor circuit have very highly coupled or correlated
parametric inter-relationships. Adding to the complexity, are
changes in the parametric values as the sizes and spacings between
the devices change. This coupling is not in the form of interaction
fields or forces but rather takes place through the correlation of
parameters between different devices. These parametric correlations
occur because of the processing of the semiconductor wafers through
its manufacturing stages.
The devices on each wafer have many n-type or p-type doped
semiconductor layers in common because of being processed at the
same temperature, or in the same gaseous environments, or in the
same implantation sessions. In addition, each doped layer has
variations over its different regions. All this results in very
complex parametric interrelationships between the various devices
within the integrated circuit. In turn these have very influential
effects on the variation of key circuit characteristics.
In spite of the tremendous importance of knowing and predicting
these relationships, accurate methods of predicting these complex
relationships between devices have evaded the semiconductor
industry. The current methods used, such as statistically
independent Monte Carlo simulation and Corner Models, either
severely underestimate or severely overestimate the variation of
key integrated circuit characteristics of interest. Either way, the
current methods are very inaccurate. In order to meet this
challenge, the methods covered in this dissertation have been
developed and applied to the case at hand. They are based on
applications of probability, statistics, stochastic, and random
field theory, and various computer algorithms.
Many of the concepts developed here can be applied to other
complex correlated systems not necessarily involving
semiconductors.
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