SHG Signal Parsing for Wafer Thickness and Defect Separation
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Solution Overview
Problem
Existing SHG metrology systems face challenges in accurately distinguishing between variations in wafer layer thickness and industrially relevant contamination or defects, leading to false positives and ambiguity in identifying problematic material properties.
Innovation Solution
Integration of secondary analysis devices and physically derived machine learning models within the SHG metrology system to parse SHG signals, accounting for layer thickness variations and isolating electrical properties, thereby enhancing signal interpretation and differentiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SHG metrology is used to detect wafer properties, then surface and interface properties can be detected with high sensitivity, but it becomes difficult to distinguish between layer thickness variations and contamination/defects
Solution Approach 1:
The patent segments the SHG signal into distinct components: one component corresponds to layer thickness variations and another component corresponds to contamination/defects. By mathematically separating these overlapping signal contributions, the system can independently analyze each property without interference from the other, thereby maintaining high detection sensitivity while improving signal interpretation accuracy.
Solution Approach 2:
The patent introduces machine learning models as intermediary components that process the raw SHG signals. These models are trained to recognize and separate the different signal components corresponding to thickness and contamination, acting as a mediator between the physical measurement and the final interpretation, thus resolving the ambiguity in signal attribution.
2Adaptability or versatility
If multiple characterization techniques are integrated into one device, then comprehensive material property analysis is achieved, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional characterization device that integrates SHG metrology with secondary analysis capabilities. This universal device can perform multiple types of measurements (surface properties, interface properties, contamination analysis, thickness measurement) using a single integrated system, thereby achieving comprehensive material property analysis while managing device complexity through unified architecture.
Solution Approach 2:
The patent merges the SHG metrology system with secondary analysis devices into a single integrated platform. By combining these characterization techniques in one device, the system achieves versatile adaptability for analyzing various material properties simultaneously, reducing the need for multiple separate instruments and simplifying the overall measurement workflow.
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
Enables clear differentiation between contamination levels and structural defects, improving the accuracy of wafer characterization and reducing false positives by normalizing SHG signals with layer thickness data, thus facilitating better material property analysis.
Implementation Method 1
Second Harmonic Generation (SHG) is a non-linear effect in which light is emitted from a material at a reflected angle with twice the frequency of an incident source light beam. The process may be considered as the combining of two photons of energy E to produce a single photon of energy 2E
Data Source
AI summary
Semiconductor metrology systems based on directing radiation on a wafer, detecting second harmonic generated (SHG) radiation from the wafer and correlating the second harmonic generated (SHG) signal to one or more electrical properties of the wafer are disclosed. The disclosure also includes parsing the SHG signal to remove contribution to the SHG signal from one or more material properties of the sample such as thickness. Systems and methods described herein include machine learning methodologies to automatically classify obtained SHG signal