US-20260130165-A1 - METHOD OF DETECTING ABNORMALITY IN SEMICONDUCTOR MANUFACTURING PROCESS AND PROGRAM FOR PERFORMING THE SAME

US20260130165A1US 20260130165 A1US20260130165 A1US 20260130165A1US-20260130165-A1

Abstract

The present invention provides a method of detecting an abnormality in a semiconductor manufacturing process by analyzing light within a process chamber that processes a substrate by using plasma. The method may comprise a cluster classification operation of clustering reference light intensity data into a plurality of clusters, the reference light intensity data varying depending on a type of a removal target removed from a substrate; a reference data generation operation of generating reference data including a distribution of abnormality determination numerical values for each of the plurality of clusters; and an abnormality detection operation of determining whether the target data is abnormal by comparing target data for detecting the abnormality with the reference data.

Inventors

Tae Kyung HA
Seung Heui LEE
Yeon Mo KIM

Assignees

PSK INC.
RTM INC.

Dates

Publication Date: 20260507
Application Date: 20251103
Priority Date: 20241107

Claims (14)

1 . A method of detecting an abnormality in a semiconductor manufacturing process by analyzing light within a process chamber that processes a substrate by using plasma, the method comprising: a cluster classification operation of clustering reference light intensity data into a plurality of clusters, the reference light intensity data varying depending on a type of a removal target removed from a substrate; a reference data generation operation of generating reference data including a distribution of abnormality determination numerical values for each of the plurality of clusters; and an abnormality detection operation of determining whether the target data is abnormal by comparing target data for detecting the abnormality with the reference data.
2 . The method of claim 1 , wherein the abnormality detection operation includes: a cluster determination operation of determining a cluster to which the target data belongs among the clusters classified in the cluster classification operation; an abnormality determination numerical value calculation operation of calculating the abnormality determination numerical values of the target data; and an abnormality determination operation of determining whether the semiconductor manufacturing process is abnormal when the target data is collected by comparing the abnormality determination numerical values of the target data with the abnormality determination numerical values of the reference data.
3 . The method of claim 2 , wherein the abnormality determination numerical values include: end point time; and end point area.
4 . The method of claim 1 , further comprising: a wavelength selection operation of selecting at least one wavelength that satisfies a reference condition among collectible wavelengths that are collectible in the process chamber as a selected wavelength.
5 . The method of claim 4 , further comprising: a selected wavelength data collection operation of collecting the reference light intensity data collected when processing the substrate for the selected wavelength.
6 . The method of claim 5 , wherein the reference condition is that a Wasserstein distance between a first section and a second section different from a first section is measured in light intensity data for each of the collectable wavelengths, the collectable wavelengths are arranged in an order in which the Wasserstein distance is large, and then the collectable wavelengths up to the n th are selected as the selected wavelengths by prioritizing the wavelength with the large Wasserstein distance.
7 . The method of claim 1 , wherein a method of the clustering used in the cluster classification operation is a K-means method or a hierarchical method.
8 . A method of detecting an abnormality in a semiconductor manufacturing process, the method comprising: determining an abnormality of the process by comparing pre-stored reference data with target data for detecting an abnormality, wherein the reference data includes distribution data for end point time and end point area.
9 . The method of claim 8 , wherein the reference data may be derived by clustering reference light intensity data that vary according to a type of a removal target to be removed from a substrate into a plurality of clusters, and calculating the end point time and the end point area of the reference light intensity data included in each cluster.
10 . A program stored in a recording medium, the program detecting an abnormality in a semiconductor manufacturing process by analyzing light within a process chamber that processes a substrate by using plasma, wherein the program performs an abnormality detection operation of determining an abnormality of the process by comparing pre-stored reference data with target data for detecting an abnormality, and the reference data includes distribution data for end point time and end point area.
11 . The program of claim 10 , wherein the program further performs a wavelength selection operation of selecting at least one wavelength that satisfies a reference condition among collectible wavelengths that are collectible in the process chamber as a selected wavelength.
12 . The program of claim 11 , wherein the program further performs: a cluster classification operation of clustering reference light intensity data for the selected wavelength into a plurality of clusters; and a reference data generation operation of generating reference data including a distribution of abnormality determination numerical values for each of the plurality of clusters.
13 . The program of claim 12 , wherein the abnormality detection operation includes: a cluster determination operation of determining a cluster to which the target data belongs among the clusters classified in the cluster classification operation; and an abnormality determination numerical value calculation operation of calculating the end point time and the end point area of the target data.
14 . The program of claim 13 , wherein the abnormality detection operation includes: an abnormality determination operation of determining whether the semiconductor manufacturing process is abnormal when the target data is collected by comparing the end point time and the end point area of the target data with the end point time and the end point area of the reference data.

Description

CROSS-REFERENCE TO RELATED APPLICATION This application claims priority to and the benefit of Korean Patent Application No. 10-2024-0157150 filed in the Korean Intellectual Property Office on November 7, 2024, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD The present invention relates to a method of detecting an abnormality in a semiconductor manufacturing process and a program for performing the same. BACKGROUND ART A semiconductor manufacturing process includes an etching process using plasma. In this process, a specific layer of a wafer surface is removed. An Optical Emission Spectroscopy (OES) analyzer is used to accurately detect an end point of the etching process. The OES analyzer is based on a specific OES wavelength generated from a decomposition product or a reaction product of etching gas according to a wafer process recipe. Accordingly, it is possible to determine an End Point Detection (EPD) time point of the process. However, background knowledge of the corresponding element is required to select an appropriate OES wavelength for each element in OES analysis. The wavelength emitted is different for each element, and accurate analysis is difficult if the wavelength is not properly matched. Furthermore, since the precision of the wavelength is different for each OES measurement equipment, the wavelength may vary in decimal units. For this reason, it is very difficult to match all wavelengths one by one and check them. In addition, even in the same process recipe, the OES reaction pattern varies depending on the layer component of the wafer. For example, the OES signal that appears when a specific layer on the wafer is etched varies depending on the material of the layer. If an abnormality is detected without classification according to the wafer layer without considering such a difference, there is a high possibility that misclassification may occur. In particular, such an error may occur when detecting an ashing rate defect. In addition, it is common to use additional monitoring means, such as sensors, in addition to OES to detect process abnormalities more effectively. For example, environmental changes may be monitored by adding a sensor that monitors the temperature or pressure in a chamber. However, this additional equipment is expensive to install and becomes a factor that increases the complexity of the process. This degrades the efficiency of the system and there is a problem that it is unreasonable in terms of cost. SUMMARY OF THE INVENTION The present invention has been made in an effort to provide a method of detecting an abnormality in a semiconductor manufacturing process that is capable of calculating end point detection time and end point detection area without knowing wavelength information of light generated when a removal target layer is removed by plasma, and a program for performing the same. The present invention has also been made in an effort to provide a method of detecting an abnormality in a semiconductor manufacturing process that is capable of detecting an abnormality in a semiconductor manufacturing process performed in a process chamber using an optical analyzer, and a program for performing the same. The problem to be solved by the present invention is not limited to the above-mentioned problems, and the problems not mentioned will be clearly understood by those skilled in the art from the present specification and the accompanying drawings. An exemplary embodiment of the present invention provides a method of detecting an abnormality in a semiconductor manufacturing process by analyzing light within a process chamber that processes a substrate by using plasma, the method including: a cluster classification operation of clustering reference light intensity data into a plurality of clusters, the reference light intensity data varying depending on a type of a removal target removed from a substrate; a reference data generation operation of generating reference data including a distribution of abnormality determination numerical values for each of the plurality of clusters; and an abnormality detection operation of determining whether the target data is abnormal by comparing target data for detecting the abnormality with the reference data. According to the exemplary embodiment, the abnormality detection operation may include: a cluster determination operation of determining a cluster to which the target data belongs among the clusters classified in the cluster classification operation; an abnormality determination numerical value calculation operation of calculating the abnormality determination numerical values of the target data; and an abnormality determination operation of determining whether the semiconductor manufacturing process is abnormal when the target data is collected by comparing the abnormality determination numerical values of the target data with the abnormality determination numerical values of the reference data