Endpoint Detection in Plasma Etching Using Multivariate OES Analysis
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Solution Overview
Problem
Current endpoint detection methods for plasma etching processes, particularly using optical emission spectroscopy (OES), face challenges in accurately determining the endpoint of etch processes, especially in conditions with low open ratios, leading to difficulties in avoiding defects like undercuts and improper etching of underlying layers.
Innovation Solution
A method involving the acquisition and processing of optical emission spectroscopy data using principal components analysis (PCA) to transform and de-mean the data, combined with a trend variable that emphasizes changes during the etch process, allowing for precise endpoint detection by analyzing the evolution of transformed data elements, even in challenging conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optical emission spectroscopy (OES) methods are used for endpoint detection, then the detection system is simple and easy to operate, but the detection precision is insufficient especially in low open ratio conditions
Solution Approach 1:
The patent transforms the OES data from raw spectral intensity values to principal component scores through mathematical transformation. This parameter change converts complex spectral data into a reduced-dimensional space where endpoint detection becomes more reliable, especially for low open ratio structures. The principal component analysis extracts the most significant variance in the data while filtering out noise and irrelevant information.
Solution Approach 2:
The patent introduces principal component analysis as an intermediary processing step between raw OES data collection and endpoint detection. This intermediary transformation layer converts the raw spectral data into a form that is more suitable for endpoint detection algorithms, acting as a mediator that bridges the gap between complex raw data and simple detection criteria.
2Reliability
If conventional OES endpoint detection algorithms are used, then the processing is fast and simple, but the reliability is low in challenging etch conditions such as low open ratios
Solution Approach 1:
The patent performs preliminary principal component analysis on training data to establish a baseline model before actual endpoint detection. The principal components and scoring algorithms are pre-computed and stored, allowing rapid application during production etching processes. This preliminary action prepares the detection system in advance, enabling fast and reliable endpoint detection during actual manufacturing without real-time computational delays.
Solution Approach 2:
The patent segments the complex OES spectral data into distinct principal components that represent different physical or chemical processes in the plasma. By dividing the data into these meaningful segments, the system can track specific components that are most indicative of endpoint conditions, improving reliability while reducing the computational burden of analyzing the entire spectrum continuously.
3Measurement precision
If more complex multivariate analysis methods are applied to OES data, then the endpoint detection precision improves, but the ease of operation decreases
Solution Approach 1:
The patent extracts only the most significant principal components from the full spectral data set, discarding less important information. This extraction approach maintains high detection precision by focusing on the key variance-carrying components while simplifying the operational complexity by reducing the number of parameters that need to be monitored and processed. The system takes out only what is necessary for accurate endpoint detection.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the robustness and reliability of endpoint detection, enabling accurate termination of the etch process and preventing defects, especially in low open ratio etching scenarios where traditional methods struggle.
Implementation Method 1
The chemical species of the gas in the plasma processing chamber are excited by the plasma excitation mechanism being used, and the excited chemical species produce distinct spectral signatures in the optical emission spectrum of the plasma.
Implementation Method 2
optical emission spectroscopy (OES) is frequently used to monitor the chemistry of the gas in the plasma processing chamber. The chemical species of the gas in the plasma processing chamber are excited by the plasma excitation mechanism being used, and the excited chemical species produce distinct spectral signatures in the optical emission spectrum of the plasma.
Data Source
AI summary
Described is a method for determining an endpoint of an etch process using optical emission spectroscopy (OES) data as an input. Optical emission spectroscopy (OES) data are acquired by a spectrometer attached to a plasma etch processing tool. The acquired time-evolving spectral data are first filtered and demeaned, and thereafter transformed into transformed spectral data, or trends, using multivariate analysis such as principal components analysis, in which previously calculated principal component weights are used to accomplish the transform. A functional form incorporating multiple trends may be used to more precisely determine the endpoint of an etch process. A method for calculating principal component weights prior to actual etching, based on OES data collected from previous etch processing, is disclosed, which method facilitates rapid calculation of trends and functional forms involving multiple trends, for efficient and accurate in-line determination of etch process endpoint.