Multi-spot laser lock-in thermography for real-time imaging of cracks in semiconductor chips during a manufacturing process

Yang, Jinyeol; Hwang, Soonkyu; An, Yun‐Kyu; Lee, Kyuhang; Sohn, Hoon

doi:10.1016/j.jmatprotec.2015.09.020

Cited by 22 publications

(9 citation statements)

References 7 publications

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“…First, the analysis area exposed to laser excitation needs to be determined within FOV because the line laser excitation may not cover the entire FOV. Assuming that the HIS system scans along the horizontal direction ( x -axis in Figure 4), the intensity profile of the line laser beam typically follows a Gaussian distribution 19 along the y -axis as shown in Figure 4. Thus, the analysis area can be determined by tracing the mid-points along the x -axis and their affected boundaries along the y -axis.…”

Section: The Deep Cnn–based Crack Evaluation Algorithmmentioning

confidence: 99%

“…The superiority of the laser IR thermography over the vision-based techniques for crack detection is that it can detect subsurface damage as well as surface damage and is robust against sensing environments by employing laser excitation sources, which has been already proven by applying it to metallic structures and semiconductor chips. 18,19 However, the excessive sensitivity to crack detection may inversely disturb precise crack evaluation in concrete because of a number of non-structural-level initial concrete cracks.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning–based autonomous concrete crack evaluation through hybrid image scanning

Jang

Kim

2019

Structural Health Monitoring

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162

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This article proposes a deep learning–based autonomous concrete crack detection technique using hybrid images. The hybrid images combining vision and infrared thermography images are able to improve crack detectability while minimizing false alarms. In particular, large-scale concrete-made infrastructures such as bridge and dam can be effectively inspected by spatially scanning the unmanned vehicle–mounted hybrid imaging system including a vision camera, an infrared camera, and a continuous-wave line laser. However, the expert-dependent decision-making for crack identification which has been widely used in industrial fields is often cumbersome, time-consuming, and unreliable. As a target concrete structure gets larger, automated decision-making becomes more desirable from the practical point of view. The proposed technique is able to achieve automated crack identification and visualization by transfer learning of a well-trained deep convolutional neural network, that is, GoogLeNet, while retaining the advantages of the hybrid images. The proposed technique is experimentally validated using a lab-scale concrete specimen with cracks of various sizes. The test results reveal that macro- and microcracks are automatically visualized while minimizing false alarms.

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Section: The Deep Cnn–based Crack Evaluation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning–based autonomous concrete crack evaluation through hybrid image scanning

Jang

Kim

2019

Structural Health Monitoring

Self Cite

162

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“…The efficiency of the filters that extract only the useful components is essential for the proper operation of the lithography and metrology tools. Unfortunately, due to aging or other factors, spectral components outside the valid spectral range can penetrate into the optical system, deteriorate its performance, and even damage it [9] [10]. That is why it is crucial to have the capability to detect the presence of out-of-useful-spectrum components.…”

Section: Introductionmentioning

confidence: 99%

Ultra-thin uncooled integrable-on-chip detector to measure wide infrared radiation residue in lithography exposure and metrology inspection tools

Jahangiri

Pawluczyk

Dąbrowski

et al. 2023

Metrology, Inspection, and Process Control XXXVII

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In modern nano-scale lithography, an essential role of the source, the illumination, and projection lenses is to deliver the precise amount of energy at a specific wavelength to the photoresist deposited on a wafer surface during exposure. Unfortunately, the source of the most advanced lithography processes may produce unwanted infrared components passing through the illumination and projection lenses and reaching the wafer surface. These infrared residues can cause local heating resulting in deformation of the optical elements and the exposed wafer, thus causing deterioration of the image quality. Some infrared spectrum components are in the band from 2 μm to 12 μm. An infrared detector that can measure only these spectral components of the exposure beam, without being affected by the much more powerful exposure spectral component, is helpful for optics diagnostic purposes and improving imaging quality. In this paper, an ultra-thin uncooled integrable-on-chip linear array infrared detector to measure the band of 2-12 μm infrared radiation is designed and fabricated based on the photovoltaic multiple junction heterostructure from VIGO Photonics, made of a HgCdTe narrow bandgap semiconductor. Features such as zero bias, low noise, and fast response, together with a wide active window, make the detector unique for use in the mid-infrared band. Besides lithography applications, the new detector can be useful in testing, inspection, and equipment using infrared sources such as: He-Ne lasers (0.6 to 4 μm), STEAM lasers (2 to 200 μm), CO2 lasers (5 to 11 μm), InGaAsP lasers (0.8 to 3 μm), and PbSnTe (3 to 20 μm) and PbSnSe (7 to 40 μm) lasers.

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“…Despite the favorable results obtained through the application of AE techniques, their effectiveness is constrained by the complex nature of geological phenomena. In the field of mining and tunnel engineering, IRT is being promoted due to its noncontact and non-coupling nature, unlike other NDT methods (Schroeder et al 2002, Yang et al 2016. The technique provides easily interpretable data in the form of images or videos, and poses no risk of radiation exposure from the hazardous sources (Yang et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Thermal monitoring and deep learning approach for early warning prediction of rock burst in underground structures

Jaiswal,

Sebastian,

Mulaveesala

2023

J. Phys. D: Appl. Phys.

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The occurrence of rockburst has the potential to result in significant economic and human losses in underground mining and excavation operations. The accuracy of traditional methods for early prediction is considerably affected by factors such as site conditions, noise levels, accessibility, and other variables. This study proposes a methodology for identifying the most defected region in a hard rock sample by integrating motion thermogram data obtained from the laboratory monitoring of rock burst phenomena with a cutting-edge deep neural network approach based on a regional convolutional network (i.e. Mask RCNN). The efficacy of the suggested approach was evaluated by determining the F1 score and average precision matrices based on a specific intersection over union value. The findings demonstrate that the proposed approach possesses satisfactory precision with respect to detection, localization, and segmentation, thereby establishing its potential utility as an autonomous predictor of rock bursts.

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Multi-spot laser lock-in thermography for real-time imaging of cracks in semiconductor chips during a manufacturing process

Cited by 22 publications

References 7 publications

Deep learning–based autonomous concrete crack evaluation through hybrid image scanning

Deep learning–based autonomous concrete crack evaluation through hybrid image scanning

Ultra-thin uncooled integrable-on-chip detector to measure wide infrared radiation residue in lithography exposure and metrology inspection tools

Thermal monitoring and deep learning approach for early warning prediction of rock burst in underground structures

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