Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Label-Efficient CT Emphysema Segmentation via Synthesis and Test-Time Training.

IEEE journal of biomedical and health informatics·2026
Same author

PRDM1-mediated epigenetic and transcriptional repression mechanisms: a key hub in immune differentiation, tumor progression, and inflammatory responses.

Frontiers in immunology·2026
Same author

Comparative machine learning approaches to prognosticate clinical outcomes in oral and maxillofacial space infections: a retrospective analysis.

BMC medical informatics and decision making·2026
Same author

DuoMod-Net: Logarithmic balancing and geometric refinement for imbalanced semi-supervised medical image segmentation.

Patterns (New York, N.Y.)·2026
Same author

Clinical large language model centered on electronic medical records.

NPJ digital medicine·2026
Same author

Predicting pulmonary nodule growth from a single time point: a fusion model of radiomics and deep learning to optimize follow-up strategies.

Journal of thoracic disease·2026

Related Experiment Video

Updated: May 10, 2026

Patterning via Optical Saturable Transitions - Fabrication and Characterization
08:19

Patterning via Optical Saturable Transitions - Fabrication and Characterization

Published on: December 11, 2014

Convolution-variation separation method for efficient modeling of optical lithography.

Shiyuan Liu1, Xinjiang Zhou, Wen Lv

  • 1Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China. shyliu@mail.hust.edu.cn

Optics Letters
|July 2, 2013
PubMed
Summary
This summary is machine-generated.

We developed convolution-variation separation (CVS), a new method for accurate and efficient optical imaging simulations. This approach speeds up calculations for various process variations, crucial for advanced lithography and metrology.

More Related Videos

Multi-step Variable Height Photolithography for Valved Multilayer Microfluidic Devices
10:18

Multi-step Variable Height Photolithography for Valved Multilayer Microfluidic Devices

Published on: January 27, 2017

Related Experiment Videos

Last Updated: May 10, 2026

Patterning via Optical Saturable Transitions - Fabrication and Characterization
08:19

Patterning via Optical Saturable Transitions - Fabrication and Characterization

Published on: December 11, 2014

Multi-step Variable Height Photolithography for Valved Multilayer Microfluidic Devices
10:18

Multi-step Variable Height Photolithography for Valved Multilayer Microfluidic Devices

Published on: January 27, 2017

Area of Science:

  • Optical imaging and simulation
  • Computational lithography
  • Metrology

Background:

  • Accurate optical imaging simulations are vital for semiconductor manufacturing and metrology.
  • Simulating a wide range of process variations can be computationally intensive and time-consuming.
  • Existing methods may struggle to balance accuracy and computational efficiency.

Purpose of the Study:

  • To introduce a novel method, convolution-variation separation (CVS), for efficient and accurate optical imaging simulations.
  • To enable faster simulations across diverse process variations without compromising precision.
  • To demonstrate the applicability of CVS in areas like inverse lithography and lens aberration metrology.

Main Methods:

  • Developed the convolution-variation separation (CVS) method based on first principles and series expansion.
  • Utilized predetermined basis functions independent of process variations.
  • Employed expansion coefficients that are dependent on specific process variations.

Main Results:

  • Demonstrated the efficiency and accuracy of the CVS method in optical image simulations.
  • Successfully applied CVS to simulate defocus and aberration variations.
  • Validated the method's effectiveness for robust inverse lithography technology and lens aberration metrology.

Conclusions:

  • The convolution-variation separation (CVS) method offers a significant advancement in optical imaging simulations.
  • CVS provides an efficient yet accurate approach for handling process variations.
  • This method has strong potential for applications in advanced semiconductor manufacturing and optical system analysis.