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As integrated circuit feature size continues to shrink, yield loss due to parametric variation will become more and more significant. Real-time feedback control provides a means of reducing parametric variation and therefore limiting this type of yield loss. A challenge in applying feedback control to semiconductor manufacturing processes is selecting the variables to feed back. In many manufacturing processes, the important product variables cannot be measured in real-time and therefore cannot be directly controlled using real-time feedback control. An alternative, which has proven effective, is to feed back process variables closely related to the product variables. Typically, selection of process variables to feed back is based upon qualitative knowledge about the process, which in many cases is limited. In this paper, an empirical methodology for selecting the best process variables for feedback in order to minimize variation in the product variables is presented. Two versions of this methodology are described, a full version and a "lite" version. The latter is an abridged version of the former. Prior to introducing this methodology, a condition under which real-time feedback control will reduce product variable variation is derived. This condition is used to highlight the sources of variance in the product variables, information which is used to explain how the methodology works. The methodology is evaluated using simulated experiments. Both the full and lite versions prove to be effective under the assumptions stated for this study although the full version is clearly superior in terms of performance. Application of the methodology to a reactive ion etch process is described in a companion work.