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Electronics
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Advances in Semiconductor Devices and Applications
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Advances in Semiconductor Devices and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Semiconductor Devices".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 1748

Special Issue Editors

Dr. Jiarui Gong

E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX 77843, USA
Interests: microelectronics; flexible semiconductor membranes and transfer-printing; semiconductor heterostructures; wide bandgap/ultrawide bandgap semiconductor devices; semiconductor surfaces and interfaces
Dr. Jie Zhou

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
Interests: semiconductor heterojunction devices; large-scale heterogeneous integration; membrane-based devices; semiconductor interface engineering; ultrawide and narrow bandgap semiconductors
Dr. Shuoyang Qiu

E-Mail Website
Guest Editor
Department of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: integrated photonics; microwave photonics; optical communication

Special Issue Information

Dear Colleagues,

The semiconductor industry is undergoing rapid changes driven by novel material and device technology, enabling significant improvements in device performance and developments in innovative applications. These advances are pushing the boundaries of technology, enhancing performance, efficiency, and the capabilities of various industries, which include electronics, telecommunications, computing, healthcare, energy, artificial intelligence, etc.

To answer the growing interest in semiconductor devices, this Special Issue aims to present up-to-date reports or reviews of advances in semiconductor devices and applications, including but not limited to the following topics:

  • Wide bandgap/Ultrawide bandgap power electronics and applications.
  • Infrared optoelectronic materials and devices.
  • Energy harvesting materials and devices.
  • Piezoelectric materials and devices.
  • Microelectromechanical systems and applications.
  • Photonic integrated devices, circuits, and applications.
  • Electro-optical, thermo-optical, and non-linear properties of photonic devices and applications.
  • Flexible electronics and applications.
  • Semiconductor membrane-based devices.
  • Low-dimensional semiconductor devices and applications.
  • Semiconductor LEDs and lasers operate from visible to mid-infrared wavelengths.
  • Semiconductor fabrication and device synthesis.
  • Semiconductor device physics and modeling.

Dr. Jiarui Gong
Dr. Jie Zhou
Dr. Shuoyang Qiu
Guest Editors

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. Electronics 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

  • transistors
  • sensors
  • optoelectronics
  • microelectromechanical systems
  • flexible electronics
  • piezoelectricity
  • low-dimensional devices
  • semiconductor optics
  • integrated optics
  • device physics

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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24 pages, 2881 KB  
Article
Wear Leveling in SSDs Considered Harmful: A Case for Capacity Variance
by Ziyang Jiao and Biyuan Yang
Electronics 2025, 14(21), 4169; https://doi.org/10.3390/electronics14214169 - 25 Oct 2025
Viewed by 404
Abstract
The trend of decreasing endurance of flash memory makes the overall lifetime of SSDs more sensitive to the effects of wear leveling. Under these circumstances, we observe that existing wear-leveling techniques exhibit anomalous behavior under workloads without clear access skew or under dynamic [...] Read more.
The trend of decreasing endurance of flash memory makes the overall lifetime of SSDs more sensitive to the effects of wear leveling. Under these circumstances, we observe that existing wear-leveling techniques exhibit anomalous behavior under workloads without clear access skew or under dynamic access patterns and produce high write amplification, as high as 5.4×, negating its intended benefits. We argue that wear leveling is an artifact for maintaining the fixed-capacity abstraction of a storage device, and it becomes unnecessary if the exported capacity of the SSD is to gracefully reduce. We show that this idea of capacity variance extends the lifetime of the SSD, allowing up to 2.94× more writes under real workloads. Full article
(This article belongs to the Special Issue Advances in Semiconductor Devices and Applications)
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Review

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36 pages, 552 KB  
Review
Review of Applications of Regression and Predictive Modeling in Wafer Manufacturing
by Hsuan-Yu Chen and Chiachung Chen
Electronics 2025, 14(20), 4083; https://doi.org/10.3390/electronics14204083 - 17 Oct 2025
Viewed by 847
Abstract
Semiconductor wafer manufacturing is one of the most complex and data-intensive industrial processes, comprising 500–1000 tightly interdependent steps, each requiring nanometer-level precision. As device nodes approach 3 nm and beyond, even minor deviations in parameters such as oxide thickness or critical dimensions can [...] Read more.
Semiconductor wafer manufacturing is one of the most complex and data-intensive industrial processes, comprising 500–1000 tightly interdependent steps, each requiring nanometer-level precision. As device nodes approach 3 nm and beyond, even minor deviations in parameters such as oxide thickness or critical dimensions can lead to catastrophic yield loss, challenging traditional physics-based control methods. In response, the industry has increasingly adopted regression analysis and predictive modeling as essential analytical frameworks. Classical regression, long used to support design of experiments (DOE), process optimization, and yield analysis, has evolved to enable multivariate modeling, virtual metrology, and fault detection. Predictive modeling extends these capabilities through machine learning and AI, leveraging massive sensor and metrology data streams for real-time process monitoring, yield forecasting, and predictive maintenance. These data-driven tools are now tightly integrated into advanced process control (APC), digital twins, and automated decision-making systems, transforming fabs into agile, intelligent manufacturing environments. This review synthesizes foundational and emerging methods, industry applications, and case studies, emphasizing their role in advancing Industry 4.0 initiatives. Future directions include hybrid physics–ML models, explainable AI, and autonomous manufacturing. Together, regression and predictive modeling provide semiconductor fabs with a robust ecosystem for optimizing performance, minimizing costs, and accelerating innovation in an increasingly competitive, high-stakes industry. Full article
(This article belongs to the Special Issue Advances in Semiconductor Devices and Applications)
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