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Microelectronic Engineering: Devices, Materials, and Technologies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 25 August 2025 | Viewed by 417

Special Issue Editor

College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
Interests: microelectronics packaging and reliability; AI for microelectronics

Special Issue Information

Dear Colleagues,

The field of microelectronic engineering has been crucial in shaping modern technology, driving advancements in communication, computation, healthcare, and beyond. As we navigate an era of unprecedented technological progress, the integration of innovative devices, advanced materials, and cutting-edge technologies has become essential to meet the increasing demands for higher performance, energy efficiency, and miniaturization.

This Special Issue, entitled ‘Microelectronic Engineering: Devices, Materials, and Technologies’, aims to provide a platform for the dissemination of innovative research and developments in this dynamic domain. The scope encompasses a wide range of topics, including, but not limited to, novel device architectures, fabrication techniques, material innovations, and their applications in emerging technologies. Contributions addressing challenges in scaling, reliability, and sustainability are encouraged

We invite original research articles, comprehensive reviews, and insightful perspectives that explore the synthesis, characterization, and application of advanced materials such as 2D materials, wide-bandgap semiconductors, and nanostructured materials. Studies on device engineering, including transistor technologies, memory devices, and optoelectronics, as well as innovations in process technologies like lithography, etching, cleaning, and advanced packaging technology, are highly welcomed. Additionally, we encourage submissions focused on semiconductor equipment technology, microelectronic reliability, and the applications of artificial intelligence (AI) in microelectronics.

This Special Issue aims to promote collaboration and exchange among researchers, engineers, and industry professionals, contributing to the collective advancement of microelectronic engineering. By bridging the gap between fundamental research and practical applications, this Special Issue seeks to inspire the next wave of technological breakthroughs.

Dr. Hu He
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • microelectronic engineering
  • transistor technologies
  • lithography
  • optoelectronics
  • process technologies
  • advanced packaging
  • semiconductor equipment
  • microelectronic reliability
  • artificial intelligence for microelectronics

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Published Papers (1 paper)

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Research

18 pages, 4803 KiB  
Article
Deep Learning-Enhanced Electronic Packaging Defect Detection via Fused Thermal Simulation and Infrared Thermography
by Zijian Peng and Hu He
Appl. Sci. 2025, 15(12), 6592; https://doi.org/10.3390/app15126592 - 11 Jun 2025
Viewed by 233
Abstract
Advancements in semiconductor packaging toward higher integration and interconnect density have increased the risk of structural defects—such as missing solder balls, pad delamination, and bridging—that can disrupt thermal conduction paths, leading to localized overheating and potential chip failure. To address the limitations of [...] Read more.
Advancements in semiconductor packaging toward higher integration and interconnect density have increased the risk of structural defects—such as missing solder balls, pad delamination, and bridging—that can disrupt thermal conduction paths, leading to localized overheating and potential chip failure. To address the limitations of traditional non-destructive testing methods in detecting micron-scale defects, this study introduces a multimodal detection approach combining finite-element thermal simulation, infrared thermography, and the YOLO11 deep learning network. A comprehensive 3D finite-element model of a ball grid array (BGA) package was developed to analyze the impact of typical defects on both steady-state and transient thermal distributions, providing a solid physical foundation for modeling defect-induced thermal characteristics. An infrared thermal imaging platform was established to capture real thermal images, which were then compared with simulation results to verify physical consistency. An integrated dataset of simulated and infrared images was constructed to enhance the robustness of the detection model. Leveraging the YOLO11 network’s capabilities in end-to-end training, dataset small-object detection, and rapid inference, the system achieved accurate and rapid localization of defect regions. Experimental results show a mean average precision (mAP) of 99.5% at an intersection over union (IoU) threshold of 0.5 and an inference speed of 556 frames per second on the simulation dataset. Training with the hybrid dataset improved detection accuracy on real images from 41.7% to 91.7%, significantly outperforming models trained on a single data source. Furthermore, the maximum temperature discrepancy between simulation and experimental measurements was less than 5%, validating the reliability of the proposed method. This research offers a high-precision, real-time solution for semiconductor packaging defect detection, with substantial potential for industrial application. Full article
(This article belongs to the Special Issue Microelectronic Engineering: Devices, Materials, and Technologies)
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