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Related Experiment Video

Updated: Oct 28, 2025

Large-area Scanning Probe Nanolithography Facilitated by Automated Alignment and Its Application to Substrate Fabrication for Cell Culture Studies

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Measurement algorithm for wafer alignment based on principal component analysis.

Ting Shu, Shaoqing Wang, Jinghao Xu

|July 15, 2021

Summary

This summary is machine-generated.

This study introduces a new wafer alignment method using principal component analysis (PCA) on mark images. PCA enhances waveform contrast, improving alignment accuracy over traditional summed projection methods.

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Area of Science:

Semiconductor manufacturing
Image processing
Metrology

Background:

Wafer alignment is critical for semiconductor fabrication processes.
Existing waveform extraction methods may not fully utilize mark image information.
Accurate alignment is essential for high-yield microchip production.

Purpose of the Study:

To propose a novel measurement algorithm for wafer alignment technology.
To enhance alignment accuracy by improving waveform extraction from mark images.
To compare the proposed method with the conventional summed projection (SP) method.

Main Methods:

Waveform extraction from enlarged mark images captured by a CCD camera.
Application of Principal Component Analysis (PCA) to mark images for position information extraction.
Comparison of PCA-based waveform extraction with the summed projection (SP) method.

Main Results:

PCA effectively extracts waveform information from mark images.
The proposed PCA method improves the contrast of the normalized waveform compared to SP.
Simulations and experiments validate the enhanced waveform contrast and improved alignment accuracy.

Conclusions:

The PCA-based method offers a more effective approach to waveform extraction for wafer alignment.
Improved waveform contrast directly leads to enhanced alignment precision.
This novel algorithm presents a significant advancement in wafer alignment technology.