As semiconductor device technology continuously shrinks, low-open area etch process prevails in front-end etch process, such as contact etch as well as one cylindrical storage (OCS) etch. To eliminate over loaded wafer processing test, it is commonly performed to emply diced small coupons at stage of initiative process development. In nominal etch condition, etch responses of whole wafer test and coupon test may be regarded to provide similar results; however, optical emission spectroscopy (OES) which is frequently utilize to monitor etch chemistry inside the chamber cannot be regarded as the same, especially etch mask is not the same material with wafer chuck. In this experiment, we compared OES data acquired from two cases of etch experiments; one with coupon etch tests mounted on photoresist coated wafer and the other with coupons only on the chuck. We observed different behaviors of OES data from the two sets of experiment, and the analytical results showed that careful investigation should be taken place in OES study, especially in coupon size etch.

In semiconductor wafer fabrication, etching is one of the most critical processes, by which a material layer is selectively removed. Because of difficulty to correct a mistake caused by over etching, it is critical that etch should be performed correctly. This paper proposes a new approach for etch endpoint detection of small open area wafers. The traditional endpoint detection technique uses a few manually selected wavelengths, which are adequate for large open areas. As the integrated circuit devices continue to shrink in geometry and increase in device density, detecting the endpoint for small open areas presents a serious challenge to process engineers. In this work, a high-resolution optical emission spectroscopy (OES) sensor is used to provide the necessary sensitivity for detecting subtle endpoint signal. Partial Least Squares (PLS) method is used to analyze the OES data which reduces dimension of the data and increases gap between classes. Support Vector Machine (SVM) is employed to detect endpoint using the data after PLS. SVM classifies normal etching state and after endpoint state. Two data sets from OES are used in training PLS and SVM. The other data sets are used to test the performance of the model. The results show that the trained PLS and SVM hybrid algorithm model detects endpoint accurately.