An auto-focusing reflection-type lens-less digital holographic microscope

2021 ◽  
Author(s):  
Zhuoshi Li ◽  
Chao Zuo ◽  
Qian Chen
Keyword(s):  
Auto Focusing

An Auto-Focus Algorithm of Fast Search Based on Combining Rough and Fine Adjustment

2012 ◽  
Vol 468-471 ◽  
pp. 534-537
Author(s):  
Zhao Hua Lin ◽  
Xin Liu ◽  
Yu Liang Zhang ◽  
Shu Mei Zhang
Keyword(s):  
Focal Plane ◽  
Narrow Range ◽  
Evaluation Function ◽  
Small Step ◽  
Uniform Sampling ◽  
Fast Search ◽  
Optical Measure ◽  
Fine Adjustment ◽  
Auto Focusing ◽  
Image Definition

A coarse and fine combined fast search and auto-focusing algorithm was suggested in this paper. This method can automatically search and find the focal plane by evaluating the image definition. The Krisch operator based edge energy function was used as the big-step coarse focusing, and then the wavelet transform based image definition evaluation function, which is sensitivity to the variation in image definition, was used to realize the small-step fine focusing in a narrow range. The un-uniform sampling function of the focusing area selection used in this method greatly reduces the workload and the required time for the data processing. The experimental results indicate that this algorithm can satisfy the requirement of the optical measure equipment for the image focusing.

Noncontact optical displacement measurements by dynamic contrast auto focusing for slow oscillatory motion

2021 ◽  
Vol 60 (12) ◽  
Author(s):  
Evgeniy Makagon ◽  
Sergey Khodorov ◽  
Anatoly Frenkel ◽  
Leonid Chernyak ◽  
Igor Lubomirsky
Keyword(s):  
Oscillatory Motion ◽  
Optical Displacement ◽  
Displacement Measurements ◽  
Auto Focusing

Free Space Realization of the Symmetrical Tunable Auto‐Focusing Lommel Gaussian Vortex Beam

2021 ◽  
pp. 2100419
Author(s):  
Jialong Tu ◽  
Xinyue Wang ◽  
Xing Yu ◽  
Haonan Wang ◽  
Dongmei Deng
Keyword(s):  
Free Space ◽  
Vortex Beam ◽  
Space Realization ◽  
Auto Focusing

Isotropic Blur Identification for Fully Digital Auto-focusing

2005 ◽  
pp. 125-132 ◽  
Author(s):  
Jeongho Shin ◽  
Sunghyun Hwang ◽  
Seong-Won Lee ◽  
Joonki Paik
Keyword(s):  
Blur Identification ◽  
Auto Focusing

Characterizing and solving imaging challenges in thick resists for wafer and panel based lithography applications

2016 ◽  
Vol 2016 (1) ◽  
pp. 000038-000043
Author(s):  
James E. Webb ◽  
Roger McCleary
Keyword(s):  
Real Time ◽  
Optimal Solution ◽  
Optical Parameters ◽  
Sem Images ◽  
Aspect Ratios ◽  
Modeling Software ◽  
Advanced Packaging ◽  
New Challenges ◽  
Large Magnification ◽  
Auto Focusing

Abstract Increasing volume using larger substrates with decreasing process margins continues to create new challenges for advanced packaging applications. Key step and repeat camera technology continues evolving for the mass production of microstructures used for 2.5D and 3D technologies. Printing dense arrays of smaller features with high aspect ratios requires higher sidewall angles in thick photoresists and polyimides. To help solve these imaging challenges we have leveraged resist modeling software and guided the adjustment of optical parameters needed for better performance. Higher contrast films have also been evaluated to help achieve the improvements in performance needed. Resist models that can include the effects of flare have been critical to understand the requirement for printing in thick negative resists and has aided in printing features on varying topography and film thicknesses. Special chucks help improve the flatness of warped wafers and real-time auto-focusing provides good image fidelity. Printing microstructures over larger formats with higher throughput has been accomplished using large magnification adjustment for improving overlay and validated by characterizing image placement errors over large substrates. Examples of resist models that are created using resist parameters and optimized using SEM images of printed features are compared. Extrapolations of the resist models have shown that guide improvements can be achieved by varying optical parameters. SEM images confirm that the modeled result of the optimal solution was achieved. Reduction in large substrate overlay error was achieved after the stage corrections were applied. Examples show that topographical errors of warped wafers can be reduced and how real-time auto-focusing for each exposure minimizes focus errors.

Power Spectra-Based Auto-Focusing Method for Airborne Optoelectronic Platform

2010 ◽  
Vol 30 (12) ◽  
pp. 3495-3500 ◽  
Author(s):  
赵志彬 Zhao Zhibin ◽  
刘晶红 Liu Jinghong
Keyword(s):  
Power Spectra ◽  
Auto Focusing

Design of Evaluation Index for Auto-Focusing Function and Optimal Function Selection

2011 ◽  
Vol 31 (4) ◽  
pp. 0418002 ◽  
Author(s):  
翟永平 Zhai Yongping ◽  
周东翔 Zhou Dongxiang ◽  
刘云辉 Liu Yunhui ◽  
刘顺 Liu Shun ◽  
彭科举 Peng Keju
Keyword(s):  
Evaluation Index ◽  
Optimal Function ◽  
Function Selection ◽  
Auto Focusing
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