The main objective of the CMP run-to-run controller is to reduce the lot-to-lot variation in the post-polish oxide film thickness. Besides tool-induced variation, product-induced variation is also a significant source of variation to the CMP process. But the need to compensate for device pattern dependencies has not been addressed until recently. In this work, two removal rate models (topography factor model and sheet film equivalent model) are compared and the equivalency between them is derived. A new control method is proposed based on the sheet film equivalent model, which shows significant improvement compared to the traditional control method based on topography factor model. The performance of the proposed control algorithm is demonstrated using both simulated and industrial examples.
In recent years, run-to-run (R2R) control technology has received tremendous interest in semiconductor manufacturing. One class of widely used run-to-run controllers is based on the exponentially weighted moving average (EWMA) statistics to estimate process deviations. Using an EWMA filter to smooth the control action on a
linear process has been shown to provide good results in a number of applications. However, for a process with severe drifts, the EWMA controller is insufficient even when large weights are used. This problem becomes more severe when there is measurement delay, which is almost inevitable in semiconductor industry. In order to control
drifting processes, a predictor-corrector controller (PCC) and a double EWMA controller have been developed. Chen and Guo (2001) show that both PCC and double-EWMA controller are in effect Integral-double-Integral (I-II) controllers, which are able to control drifting processes. However, since offset is often within the noise of the process, the second integrator can actually cause jittering. Besides, tuning the second filter is not as intuitive as a single EWMA filter. In this work, we look at an alternative way Recursive Least Squares (RLS), to estimate and control the drifting process. EWMA and double-EWMA are shown to be the least squares estimate for locally constant mean model and locally constant linear trend model. Then the recursive least squares with exponential factor is applied to shallow trench isolation etch process to predict the future etch rate. The etch process, which is a critical process in the flash memory manufacturing, is known to suffer from significant etch rate drift due to chamber seasoning. In order to handle the metrology delay, we propose a new time update scheme. RLS with the new time update method gives very good result. The estimate error variance is smaller
than that from EWMA, and mean square error decrease more than 10% compared to that from EWMA.