Feedforward control for reduced run-to-run variation in microelectronics manufacturing

Abstract

Increased manufacturing yields can be obtained by reducing process variation. One potential method to achieve lower process variance is through interprocess feedforward control. During feedforward control, a process recipe is adjusted to compensate for measured input deviations. The potential benefits of feedforward control include reduced run-to-run variance, rework, and scrap. Feedforward control has been used often in manufacturing. However, there are two problematic issues associated with feedforward recipe adjustment: 1) there is noise in the measurement tool and adjusting for inaccurate measurements could increase the variance and 2) it is difficult to alter one parameter in a manufacturing process without worsening other key parameters. In this paper, we will address both issues using a systems approach. Measurement noise poses a significant threat to the success of feedforward control. If the measurement noise is sufficiently large, the variance under feedforward control could exceed the variance with no control. To address this concern, we have integrated statistics theory into the feedforward controller design. This detunes the recipe adjustment based on the confidence in the accuracy of the sensor. These algorithms have the effect of filtering the noise from the measurement tool. In order to address the problem of altering one parameter without adversely affecting others, one can use a feedforward controller that selects a recipe from within a predefined set of allowable qualified recipes. We call this feedforward recipe selection control (FRSC). We have developed a design methodology for this type of controller. Preliminary versions of our design algorithms have been implemented into a graphical user interface (GUI)-based computer-aided design (CAD) environment. This interactive software package guides the engineer through the design of feedforward controllers using process data as inputs.