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High-resolution X-ray imaging for industrial process monitoring and quality control
E. Zschech 1, M.V. Chukalina 2, K.B. Bulatov 2

1 Brandenburg University of Technology,
03046, Germany, Cottbus, Platz der Deutschen Einheit 1, Konrad-Zuse-Strasse 1;
2 Federal Research Center "Computer Science and Control", RAS,
119333, Russia, Moscow, Vavilov street 44 b. 2

  PDF, 1967 kB

DOI: 10.18287/COJ1803

Страницы: 1138-1147.

Язык статьи: English.

Аннотация:
High-resolution X-ray imaging is an essential component of advanced workflows for industrial process monitoring and quality control (e.g., for metrology and defect inspection in the semiconductor industry). Depending on the specific application area, however, it is subject to different requirements, particularly regarding imaging accuracy and reconstruction fidelity, which are analyzed and systematically structured in this study. As an example, a seamless workflow of two nondestructive techniques with different spatial resolution and different throughput (here shown for a combination of acoustic and X-ray techniques) is proposed to auto-detect and auto-classify defects. X-ray microcopy and high-resolution X-ray computed tomography (XCT) provide nondestructive characterization capabilities on opaque objects, observing features with sizes down to several 10 nanometers. Because of the ability of micro-XCT and nano-XCT to reveal structural characteristics, to determine deviations from a well-defined standard, or to observe kinetic processes, they are suitable imaging techniques for micro- and nano-structured objects, but also for industrial process monitoring and quality control. Typical applications of high-resolution XCT are categorized into 3 groups: 1) Structure analysis – Creation of 3D digital images of the complete interior structure of an opaque object, 2) Flaw detection – Monitoring industrial processes and defect inspection, and 3) Quality control – Observing kinetic processes in objects important for industrial quality control and reliability engineering. These different categories of applications have different requirements for the accuracy of the 3D reconstruction and for the time-to-data. While the highest possible resolution is requested for group 1, data acquisition and data analysis time are essential for group 2. To get high-resolution 3D information of the complete interior structure of an opaque object using lens-based laboratory nano-XCT requires a thorough data analysis, e.g., the application of deep convolutional neural networks, for denoising and mitigation of artefacts. Kinetic studies for group 3, e.g., of reliability-limiting degradation processes in microchips, provide the opportunity to establish appropriate risk mitigation strategies to avoid catastrophic failure.
      The rapid evolution of advanced semiconductor technologies, including technologies for heterogeneous 3D integration of ICs and chiplet architectures, provides significant challenges for metrology, defect inspection, and physical failure analysis (PFA). The application of nano-XCT as a highly reliable inspection method requires a balance between throughput and fault detection (i.e., measurement and reconstruction accuracy). Ways to achieve a drastic increase in acquisition speed include high-brilliance laboratory X-ray sources, the application of AI algorithms for new image acquisition protocols, and high-speed data processing. A thorough and systematic analysis of the accuracy needed and the consequences for protocol and data analysis will support the goal of the semiconductor industry to improve throughput in metrology and defect inspection.
     This work may be of interest to a broad audience, including both specialists in the field of XCT and professionals employing XCT as a tool for industrial applications.

Ключевые слова:
computed tomography, reconstruction algorithms, reconstruction accuracy, metrology, defect inspection, reliability engineering.

Citation:
Zschech E, Chukalina MV, Bulatov KB. High-resolution X-ray imaging for industrial process monitoring and quality control. Computer Optics 2025; 49(6): 1138-1147. DOI: 10.18287/COJ1803.

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