A main theme of the work done in the microchip simulation laboratory is to use simulation of physical processes to aid in improving the design and manufacturability of microelectronic related products. A key motivating factor here is that experimentation is generally very expensive. Consequently, if simulation exists that is sufficiently accurate, fast, and easy to use, then it can save considerable expense for developing technologies, improving existing ones, and making current technologies more manufacturable. The research in this lab focuses on several fronts, namely, to obtain the best physical description of the physical processes involved in the technology, to develop algorithms that are numerically accurate, robust, and fast, and to combine these aspects, with appropriate tradeoffs, into simulation packages that aid in the overall process of microelectronic technology development and manufacturability. Much of the current research of this lab focuses on microlithography, but other work has also begun here on nanoelectronics, and even into more fundamental aspects involving the interaction and description of carrier transport and electromagnetic fields.

     One unifying aspect of our work is the belief that "simulation" is our best means for making predictions about future possible experiments. Here we mean "simulation" in a very global sense, and not "simulation" as pertains to some specific academic or commercial program that a technologist may or may not have found reliable. Simulation, in the broad sense, is an encoding of available information about some phenomena, and the structuring of it in such a way as to enable the best available replication, interpolation, or extrapolation of the observed phenomena under similar or different conditions. This "encoding of available information," means our best encapsulation of experimental measurements and theoretical knowledge, combined. If there is some new aspect of the phenomena that has been discovered, then what better way to record that knowledge than to fold it into the knowledge base and logic incorporated into a "simulation tool?" If a particular simulation tool is not "reliable," then either the phenomena is not well understood or characterized to begin with, perhaps over the regime of data in question, or the implementation of the available knowledge about the experiment has not been adequately carried out. Regarding the latter, we note that poor implementation can cover a wide range of categories, from very simple, basic coding mistakes, to user-friendly issues, to the very difficult problems of numerical robustness, stability, and convergence.

     Our emphasis on simulation here should not be perceived as meaning we believe simulation is some sort of magical panacea. Of course, continued experimentation in new areas is absolutely essential. The right balance between simulation and experimentation is a difficult question to answer, and depends on many factors, such as the sophistication of the simulation program with respect to the phenomena being examined, the skill in using the program, the experimental factors that can be controlled, the skill and expertise of the experimenter, the availability of equipment, the relative expenses of the two approaches, etc. In general, both simulation and experimentation help each other test, confront, confirm, and probe each other's predictions and results. Often one or the other will produce results that are puzzling and in the end turn out to be mistakes in procedures; continued checks help to ensure that long periods of time are not spent on wrong directions. Some experimental results are absolutely critical for providing calibration or model parameters to a simulator. Some simulation results yield intermediate physical results that are nearly impossible to obtain experimentally, but that yield enormous physical insight into the inner workings of the mechanisms involved in the phenomena.

     Nevertheless, having made this point on experimentation, we again emphasize, that the use of simulation, in the most general sense, does indeed appear to be our best means available for encoding available information, obtained by whatever means, and using that information to make predictions on physical processes and experiments. Moreover, by making the simulation tools as user friendly as possible, then enormous gains can be obtained for designing and improving present and future technologies. Indeed, one may even argue that if these goals of an adequate simulator have been realized, then not using simulation tools results in inefficient business practices and a severe handicap as compared with competing technologists who make solid and effective use of simulation methods.

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