Micro-crack defects detection of semiconductor Si-wafers based on Barker code laser infrared thermography

Gao

et al. 2022

Preprint

Self Cite

Honeycomb structures are widely used in aerospace and other fields, which are prone to debonding defects during service. Therefore, it is necessary to detect the defects of honeycomb structures in time. In this paper, the square-wave thermography detection principle was studied. The thermal conduction of CFRP / Al honeycomb panel with debonding defects was simulated by the finite element analysis. Pulse phase thermography (PPT), principal component analysis (PCA), thermal signal reconstruction (TSR), and total harmonic distortion (THD) ware studied for the original infrared image sequence. The signal-to-noise ratio (SNR) was calculated and the processing results ware compared. PCA-PC1 had the maximum SNR, which could reduce the background noise interference in square-wave thermography. Square-wave thermography can accurately detect and identify the debonding defects of the honeycomb structure.

Section: Simulation Results and Analysismentioning

confidence: 99%

Study on debonding defects detection of CFRP / Al honeycomb structure by Square-wave thermography

Gao

et al. 2022

Preprint

Self Cite

“…To objectively evaluate the assay’s performance, signal-to-noise ratio (SNR) [ 30 ] is a widely used metric in thermography data processing. In this study, SNR is employed to assess the effectiveness of the proposed approach.…”

Section: Methodsmentioning

confidence: 99%

Generative Deep Learning-Based Thermographic Inspection of Artwork

Wang

Jiang

et al. 2023

Sensors

Infrared thermography is a widely utilized nondestructive testing technique in the field of artwork inspection. However, raw thermograms often suffer from problems, such as limited quantity and high background noise, due to limitations inherent in the acquisition equipment and experimental environment. To overcome these challenges, there is a growing interest in developing thermographic data enhancement methods. In this study, a defect inspection method for artwork based on principal component analysis is proposed, incorporating two distinct deep learning approaches for thermographic data enhancement: spectral normalized generative adversarial network (SNGAN) and convolutional autoencoder (CAE). The SNGAN strategy focuses on augmenting the thermal images, while the CAE strategy emphasizes enhancing their quality. Subsequently, principal component thermography (PCT) is employed to analyze the processed data and improve the detectability of defects. Comparing the results to using PCT alone, the integration of the SNGAN strategy led to a 1.08% enhancement in the signal-to-noise ratio, while the utilization of the CAE strategy resulted in an 8.73% improvement.

“…Analyzing thermal images aids in defect identification, with laser excitation being the most frequently used method for semiconductor chip defect detection among various external excitation techniques. Bu et al [151] investigated a method utilizing Barker code-modulated pulse compression waveforms for detecting microcrack defects in semiconductor silicon wafers. This technique employed an optical infrared thermal imaging device for transmission, where an infrared camera captured the thermal wave signal response to the laser-modulated Barker code waveform.…”

Section: Chipmentioning

confidence: 99%

Progress in Active Infrared Imaging for Defect Detection in the Renewable and Electronic Industries

Zhao,

et al. 2023

Sensors

In recent years, infrared thermographic (IRT) technology has experienced notable advancements and found widespread applications in various fields, such as renewable industry, electronic industry, construction, aviation, and healthcare. IRT technology is used for defect detection due to its non-contact, efficient, and high-resolution methods, which enhance product quality and reliability. This review offers an overview of active IRT principles. It comprehensively examines four categories based on the type of heat sources employed: pulsed thermography (PT), lock-in thermography (LT), ultrasonically stimulated vibration thermography (UVT), and eddy current thermography (ECT). Furthermore, the review explores the application of IRT imaging in the renewable energy sector, with a specific focus on the photovoltaic (PV) industry. The integration of IRT imaging and deep learning techniques presents an efficient and highly accurate solution for detecting defects in PV panels, playing a critical role in monitoring and maintaining PV energy systems. In addition, the application of infrared thermal imaging technology in electronic industry is reviewed. In the development and manufacturing of electronic products, IRT imaging is used to assess the performance and thermal characteristics of circuit boards. It aids in detecting potential material and manufacturing defects, ensuring product quality. Furthermore, the research discusses algorithmic detection for PV panels, the excitation sources used in electronic industry inspections, and infrared wavelengths. Finally, the review analyzes the advantages and challenges of IRT imaging concerning excitation sources, the PV industry, the electronics industry, and artificial intelligence (AI). It provides insights into critical issues requiring attention in future research endeavors.