A robust SEM auto-focus algorithm using multiple band-pass filters

Harada, Masaru; Obara, Kenji; Nakamae, Koji

doi:10.1088/1361-6501/28/1/015403

Cited by 8 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. ., 25, p = 13) in ( 5) generates sets of simulated sharpness evaluation curves [36], where j, p, σ f , and α denote the image stack index, focus position, Gaussian standard deviation, and offset, respectively. A normally distributed random number noise(ε f ) generates the noise bias with mean 0 and standard deviation ε f to simulate local extrema.…”

Section: Simulation Analysis Of Posementioning

confidence: 99%

Enhancing Autofocus in Non-Mydriatic Fundus Photography: A Fast and Robust Approach with Adaptive Window and Path-Optimized Search

Liu,

Qiu,

Cai

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Non-mydriatic fundus photography (NMFP) plays a vital role in diagnosing eye diseases, with its performance primarily dependent on the autofocus process. However, even minor maloperations or eye micro-movements can compromise fundus imaging quality, leading to autofocus inaccuracy and a heightened risk of misdiagnosis. To enhance the autofocus performance in NMFP, a fast and robust fundus autofocus method with adaptive window and path-optimized search is proposed. In this method, the adaptive focus window is used to suppress irrelevant image contents and correct the sharpness curve, and the path-optimized search is constructed to overcome the curve’s local extrema, in order to achieve rapid focus position convergence. This method was simulated and clinically studied with the self-developed autofocus system for NMFP. The results of 80 cases of human eye imaging show that, compared with similar autofocus methods, this method achieves a focus success rate of 90% with the least axial scanning, and can adapt to non-ideal imaging conditions such as pupil misalignment, eyelash occlusion, and nystagmus.

show abstract

Section: Simulation Analysis Of Posementioning

confidence: 99%

Enhancing Autofocus in Non-Mydriatic Fundus Photography: A Fast and Robust Approach with Adaptive Window and Path-Optimized Search

Liu,

Qiu,

Cai

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…As the technology nodes shrink below 7 nm, killer defect sizes on both wafers and masks become even smaller, making it difficult for SEM-based inspection systems to automatically capture clear images of different types of defects. Harada et al [55] have proposed an auto-focus algorithm using multiple band-pass filters. They proposed an index 'focus measure' that indicates the focussing state of the PE beam and a method for designing a bandpass filter.…”

Section: Detection Review and Automatic Classification Of Defects And...mentioning

confidence: 99%

Electron microscopy in semiconductor inspection

Nakamae

2021

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

Currently, semiconductor devices are manufactured in a technology node of several nanometers. Electron microscopy is mainly used in semiconductor inspection in manufacturing stages since accelerated electrons have wavelengths of nanometers or less, and a high spatial resolution can be expected. Among various electron microscopes since the scanning electron microscope (SEM) can observe the sample as it is without processing the sample, the SEM-based inspection instrument is mainly used at each stage of manufacturing the semiconductor device. The paper presents a review of SEM-based electron microscopy in semiconductor inspection. First, an overview of electron microscopy is described to understand the electron-sample interaction, the characteristics of electrons emitted from an irradiated specimen, charging, noise, and so on. Next, application areas such as mask inspection are introduced. Finally, future challenges are discussed.

show abstract

“…DiMeo, et al 6 proposed autofocus control system based on Gaussian shaped focus measure curve and adaptive hill-climbing method. Elsewhere, Harada, et al 7 developed a closed-loop autofocus system for SEM through a customized bandpass filter. Moreover, recently, deep learning-based autofocus systems that have shown powerful performance in image processing are being actively studied 8 – 10 .…”

Section: Introductionmentioning

confidence: 99%

Robust autofocusing for scanning electron microscopy based on a dual deep learning network

Lee

Nam

Kim³

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Scanning electron microscopy (SEM) is a high-resolution imaging technique with subnanometer spatial resolution that is widely used in materials science, basic science, and nanofabrication. However, conducting SEM is rather complex due to the nature of using an electron beam and the many parameters that must be adjusted to acquire high-quality images. Only trained operators can use SEM equipment properly, meaning that the use of SEM is restricted. To broaden the usability of SEM, we propose an autofocus method for a SEM system based on a dual deep learning network, which consists of an autofocusing-evaluation network (AENet) and an autofocusing-control network (ACNet). The AENet was designed to evaluate the quality of given images, with scores ranging from 0 to 9 regardless of the magnification. The ACNet can delicately control the focus of SEM online based on the AENet’s outputs for any lateral sample position and magnification. The results of these dual networks showed successful autofocus performance on three trained samples. Moreover, the robustness of the proposed method was demonstrated by autofocusing on unseen samples. We expect that our autofocusing system will not only contribute to expanding the versatility of SEM but will also be applicable to various microscopes.

show abstract

A robust SEM auto-focus algorithm using multiple band-pass filters

Cited by 8 publications

References 9 publications

Enhancing Autofocus in Non-Mydriatic Fundus Photography: A Fast and Robust Approach with Adaptive Window and Path-Optimized Search

Enhancing Autofocus in Non-Mydriatic Fundus Photography: A Fast and Robust Approach with Adaptive Window and Path-Optimized Search

Electron microscopy in semiconductor inspection

Robust autofocusing for scanning electron microscopy based on a dual deep learning network

Contact Info

Product

Resources

About