Submit to Special Issue Submit Abstract to Special Issue Review for Micromachines Propose a Special Issue

Journal Menu

Journal Browser

Emerging Technologies and Applications for Semiconductor Industry

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 9652

Share This Special Issue

Special Issue Editors

Prof. Dr. Nian Cai

E-Mail Website
Guest Editor

School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: artificial intelligence and related applications in intelligent manufacturing; industrial inspection; safety surveillance; biomedicine; and new energy

Prof. Dr. Han Wang

E-Mail Website
Guest Editor

School of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: micro-electronic manufacturing technology and equipment; micro-nano manufacturing and equipment

Prof. Dr. Zuyong Wang

E-Mail Website
Guest Editor

College of Materials Science and Engineering, Hunan University, Changsha 410082, China
Interests: advanced materials for flexible electronic technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Semiconductors are the backbone of modern electronics, driving advancements in fields such as computing, telecommunications, and energy storage. This Special Issue will focus on the technologies and applications for the semiconductor industry, especially on materials, design, processing, components, devices, equipment, systems, etc., to push the development of the semiconductor industry. Topics of interest include, but are not limited to, new material developments, materials characterization techniques, processing technologies, semiconductor component and device design, data analysis, semiconductor testing and metrology, and advanced semiconductor equipment and systems. This Special Issue will serve as a comprehensive resource for researchers, engineers, and industry professionals interested in semiconductor advancements.

We invite you to submit original research articles or reviews that explore semiconductor industrial technologies and applications in the following areas, but not limited to, the following:

Semiconductor Materials for Emerging Technologies:

- Novel semiconductor materials such as wide bandgap semiconductors for power electronics and optoelectronics.

- Two-dimensional materials and their integration in nanoelectronics.

- Organic semiconductors for flexible and printed electronics.

Advanced Semiconductor Processing and Fabrication Techniques:

- Material synthesis and deposition methods for high-performance semiconductors.

- Patterning techniques such as nanoimprint lithography and their impact on material properties.

- Advanced etching and doping technologies for fine-tuning material characteristics.

Materials for Semiconductor Devices:

- High-k dielectrics, gate materials, and their impact on transistor performance.

- Three-dimensional integration and packaging materials for next-generation semiconductor devices.

- Materials for memory devices, including resistive switching materials, ferroelectrics, and phase-change materials.

Characterization and Modeling of Semiconductor Materials:

- Advanced techniques for material characterization.

- Computational models for predicting material properties and device performance.

- In situ characterization for real-time material behavior monitoring during fabrication.

Sustainability and Recycling of Semiconductor Materials:

- Materials for environmentally friendly semiconductor manufacturing processes.

- Strategies for recycling and reusing semiconductor materials in the context of sustainability.

Semiconductor Components and Equipment:

-Advanced semiconductor manufacturing equipment, including deposition, etching, lithography, and packaging systems.

-Materials and design of critical components for semiconductor equipment, such as chambers, electrodes, targets, and gas distribution systems.

-Innovations in thermal management, plasma sources, and power delivery systems for high-performance semiconductor fabrication.

-Reliability, lifetime improvement, and failure analysis of key equipment components under extreme operating conditions.

Semiconductor Testing and Metrology:

-Advanced inspection and metrology techniques for nanoscale semiconductor devices.

-In-line and real-time monitoring methods for process control and defect detection.

-Development of non-destructive testing approaches for semiconductor materials, components, and integrated devices.

-Machine learning and AI-driven methods for data analysis in semiconductor testing and characterization.

Prof. Dr. Nian Cai
Prof. Dr. Han Wang
Prof. Dr. Zuyong Wang
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 250 words) can be sent to the Editorial Office for assessment.

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. Micromachines is an international peer-reviewed open access monthly 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 2100 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

emerging semiconductor materials
advanced processing and fabrication techniques
materials for semiconductor devices
semiconductor material characterization
sustainability and recycling
semiconductor components and equipment
semiconductor testing and metrology

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
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.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (14 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

23 pages, 7338 KB

Open AccessArticle

Intelligent Optimization of Gas-Assisted Electrospinning via LLM-Guided Bayesian Inference

by Jun Zeng, Rongguang Zhang, Weicheng Ou, Xuanzhi Zhang, Shize Huang, Xun Chen and Guojie Xu

Micromachines 2026, 17(5), 619; https://doi.org/10.3390/mi17050619 (registering DOI) - 18 May 2026

Abstract

Nanofiber-based structures have shown considerable potential in semiconductor-related applications, including ultra-thin dielectric layers and flexible electronic devices, owing to their tunable micro-/nanoscale morphology. However, the manufacturing of these structures is often hindered by the complex multiparameter coupling and poor reproducibility inherent in conventional electrospinning processes. To address these challenges, this study develops an intelligent optimization framework for gas-assisted electrospinning by integrating Large Language Models (LLMs) with Bayesian Optimization (BO). A Gaussian Process Regression (GPR) surrogate model was established to navigate the high-dimensional parameter space efficiently. Comparative studies demonstrate that the proposed BO+LLM strategy not only outperforms pure data-driven BO and pure knowledge-driven LLM approaches but also surpasses the conventional Response Surface Methodology (RSM) baseline, successfully locating a verified minimum fiber diameter of 239 nm. Furthermore, through response-surface analysis, this work identifies a specific multiphysics collaborative window where electrostatic stretching and aerodynamic assistance are balanced. These findings provide a robust pathway for the reproducible fabrication of nanofiber-based electronic devices. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications for Semiconductor Industry)

19 pages, 6565 KB

Open AccessArticle

Simulation and Response Surface Methodology for Predicting Mass Transfer in Coaxial Electrospun Core-Shell Fibers

by Xun Chen, Weiming Shu, Rongguang Zhang, Shize Huang and Xuanzhi Zhang

Micromachines 2026, 17(5), 606; https://doi.org/10.3390/mi17050606 (registering DOI) - 15 May 2026

Viewed by 140

Abstract

Coaxial electrospinning technology enables the fabrication of nanofibers with a core-shell structure, thereby facilitating the encapsulation of functional materials. Its efficacy lies in the precise regulation of mass transfer behavior at the sensing interface. However, achieving the controllable preparation of core-shell fiber structures in complex environments and quantitatively predicting their mass transfer kinetics remain challenging. This study aims to establish a predictive framework combining simulation and experiment. Firstly, finite element simulations using COMSOL clarified that increasing the shell thickness or decreasing its effective diffusion coefficient can significantly delay analyte transport. A model incorporating time-varying parameters further revealed the influence of polymer swelling on the initial release kinetics. Using the diffusion of an aqueous KCl solution as a model system, experiments confirmed that increasing the shell solution concentration is an effective processing strategy for enhancing the mass transfer barrier. Based on the Box-Behnken design and response surface methodology (RSM), a quantitative model linking key process parameters to release kinetic parameters was established. Model diagnostics indicated that the regression equation is significant and reliable. Validation experiments demonstrated that the model possesses good predictive capability for the key release kinetic parameters, with prediction errors within an acceptable range. The framework established in this study indicates that active design of the mass transfer behavior of core-shell fibers can be achieved through process control, providing a quantitative predictive tool and methodological reference for the preparation of controllable mass transfer interfaces for sensing applications. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications for Semiconductor Industry)

► Show Figures

Figure 1

19 pages, 12679 KB

Open AccessArticle

Lightweight Semantic-Guided FCOS for In-Line Micro-Defect Inspection in Semiconductor Manufacturing

by Tao Zhang, Shichang Yan and Gaoe Qin

Micromachines 2026, 17(4), 473; https://doi.org/10.3390/mi17040473 - 14 Apr 2026

Viewed by 514

Abstract

The relentless miniaturization of semiconductor components and Printed Circuit Boards (PCBs) has rendered Automated Optical Inspection (AOI) of micro-defects a critical bottleneck in modern manufacturing and metrology. While in-line inspection systems offer economically viable and scalable quality control solutions, they impose stringent constraints on both inference latency and detection robustness—particularly for diminutive, sparsely distributed defects (e.g., mouse bites, pinholes) amidst complex, repetitive circuit topologies. To bridge this gap, we present a semantic-enhanced FCOS framework specifically engineered for micro-defect inspection. Our approach introduces two synergistic innovations: (1) a Semantic-Guided Upsampling Unit (SGU) that adaptively reweights channel–spatial features to reconcile the semantic disparity between shallow textural details and deep contextual representations; and (2) a Sparse Center-ness Calibration (SCC) module that enforces high-confidence, spatially sparse supervision to sharpen localization precision and suppress false positives. The SGU is integrated within a Progressive Semantic-Enhanced Feature Pyramid Network (PSE-FPN) that extends multi-scale representations to stride-4 (P2) resolution, while the SCC module is embedded directly into the detection head. Comprehensive evaluations on MS COCO and the real-world DeepPCB dataset validate the efficacy of our design. On COCO, our model achieves 41.8% AP with real-time throughput of 28 FPS on a single NVIDIA 1080Ti GPU. A lightweight variant further attains 41.6% AP at 42 FPS, accommodating high-throughput production environments. For PCB defect detection, the framework delivers 98.7% mAP@0.5, substantially outperforming contemporary detectors. These results demonstrate that semantics-aware, lightweight architectures enable scalable, real-time quality assurance in semiconductor manufacturing. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications for Semiconductor Industry)

► Show Figures

Figure 1

15 pages, 3191 KB

Open AccessArticle

High-Uniformity Core-Shell Nanofibers for Semiconductor Packaging: Process Optimization and Performance Study of Airflow-Assisted Coaxial Electrospinning

by Xun Chen, Shize Huang, Rongguang Zhang, Xuanzhi Zhang, Jiecai Long and Guohuai Lin

Micromachines 2026, 17(4), 463; https://doi.org/10.3390/mi17040463 - 10 Apr 2026

Viewed by 383

Abstract

Semiconductor miniaturization demands stricter material uniformity. Core-shell nanofibers, promising for semiconductor packaging and flexible circuits, face application limits due to traditional coaxial electrospinning’s electric field instability—causing poor fiber diameter uniformity and challenges with high-viscosity and low-conductivity solutions. To address this, airflow-assisted coaxial electrospinning leveraged airflow-electric field synergy to enhance fiber stretching. COMSOL Multiphysics 6.4 simulated the influence of different inner diameters of the air flow nozzles on the air flow field, while the response surface method optimized parameters. At 10 kPa air pressure, 16.71 kV voltage, and a gas nozzle inner diameter of 3.42 mm, nanofibers showed regular morphology with a diameter coefficient of variation as low as 9.2%. This study enables stable preparation of highly uniform core-shell nanofibers, providing key process support for their large-scale semiconductor application and advancing flexible electronics and photodetection. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications for Semiconductor Industry)

► Show Figures

Figure 1

12 pages, 2082 KB

Open AccessArticle

Design and Experimental Validation of a Dynamic Frequency Sweeping Algorithm for Optimized Impedance Matching in Semiconductor RF Power Systems Under Pulse-Mode Operation

by Zhaolong Fan, Zhifeng Wang, Long Xu, Lili Hou, Long Yao, Siao Zeng and Mingqing Liu

Micromachines 2026, 17(3), 376; https://doi.org/10.3390/mi17030376 - 20 Mar 2026

Viewed by 539

Abstract

The design and implementation of a dynamic frequency sweeping algorithm for a 3 kW RF power source are underpinned by theoretical principles aimed at optimizing impedance matching under pulse-mode operation. The algorithm dynamically adjusts the output frequency within a predefined range to align the source impedance Z_source with the conjugate of the load impedance Z*_load, maximizing the power transfer efficiency and minimizing the reflection coefficient Γ. This is achieved by leveraging the maximum power transfer theorem and adapting to dynamic load variations, such as those induced by the plasma state transitions. The algorithm incorporates adaptive step size adjustments based on the rate of change of Γ, predictive frequency initialization using historical data, and real-time impedance monitoring to ensure efficient convergence within the constrained pulse “ON” time (TON). Integration with pulse mode requires synchronization with the pulse signal, fast convergence, and optimized search strategies. Experimental validation on a 13.56 MHz, 3 kW Automatic Sweep Generator testbed operating at 20 kHz pulse modulation with a 50% duty cycle demonstrates a linear and stable sweep, achieving impedance matching and low reflected power within 5.0172 ms. These findings highlight the algorithm’s potential for high-precision applications, such as RF plasma excitation, and underscore the importance of adaptive techniques in dynamic RF systems. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications for Semiconductor Industry)

► Show Figures

Figure 1

25 pages, 2611 KB

Open AccessArticle

Noise-Robust Wafer Map Defect Classification via CNN-ESN Hybrid Architecture

by Hayeon Choi, Dasom Im, Sangeun Oh and Jonghwan Lee

Micromachines 2026, 17(3), 309; https://doi.org/10.3390/mi17030309 - 28 Feb 2026

Viewed by 621

Abstract

Wafer map defect classification plays a critical role in yield monitoring and root-cause analysis in semiconductor manufacturing. Although recent convolutional neural network (CNN)-based approaches have achieved high classification accuracy, most existing models are evaluated primarily on clean datasets and remain vulnerable to unseen perturbations and representation-level variability at test time. In this paper, we propose a hybrid CNN–echo state network (ESN) architecture that integrates spatial feature extraction with sequential aggregation to enhance robustness under input perturbations. The CNN backbone extracts two-dimensional feature maps, which are converted into ordered sequences using a multidirectional scanline strategy and processed by an ESN reservoir. The resulting sequential representations are combined with CNN features through a class-specific adaptive fusion mechanism. Using the defect-only eight-class version of the WM-811K dataset, we systematically evaluate robustness under multiple perturbation scenarios, with particular focus on the clean train/noisy test (CT-NT) setting. To ensure a controlled robustness evaluation aligned with the binary nature of wafer map data, we introduce binary-consistent die-flip perturbations and additionally employ additive Gaussian perturbations as a representation-level stress test. Under clean-data conditions, the proposed model showed a 0.61 pp improvement in test accuracy compared to the ResNet34-based CNN, with notably larger gains for rare classes and defect types exhibiting strong structural patterns. In the clean train/noisy test scenario, where the model was trained on clean wafer map data and evaluated under controlled test-time perturbations, the accuracy of the CNN baseline dropped to 77.59% at

σ

= 0.10, whereas the proposed hybrid model maintained an accuracy of 87.30%, resulting in an absolute improvement of 9.71 pp. Per-class analysis reveals that the robustness gain is class-dependent, with pronounced improvements for defect types exhibiting clear and repetitive structural patterns, such as Loc and Edge-Ring. Further mechanistic analysis demonstrates that the robustness improvement arises from enhanced representation stability and bounded reservoir dynamics, rather than from changes in CNN feature extraction or training regularization. These results demonstrate that the proposed CNN-ESN hybrid architecture provides meaningful advantages in terms of robustness under noisy evaluation conditions without requiring noise-aware training or prior knowledge of perturbation characteristics. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications for Semiconductor Industry)

► Show Figures

Figure 1

22 pages, 3343 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Post-Release Metallization in MEMS Silicon-to-Silicon Contact Switches for On-Resistance Improvement

by Abdurrashid Hassan Shuaibu, Almur A. S. Rabih, Yves Blaquière and Frederic Nabki

Micromachines 2026, 17(3), 288; https://doi.org/10.3390/mi17030288 - 26 Feb 2026

Viewed by 1504

Abstract

This work reports a post-release sputter-metallization process for microelectromechanical systems (MEMS) switches with silicon-to-silicon (Si-to-Si) contacts fabricated by deep reactive ion etching. Platinum (Pt) was selectively deposited on the contacting platforms through a perforated mask. Alternatively, aluminum (Al) was deposited over a thin [...] Read more.

Ω

at a 1

m

A

test current, whereas the resistance of Al/Cr coatings decreased from

7.94

k

Ω

at 1

m

A

to 270

Ω

at 25

m

A

, a change that was potentially linked to oxidation of the Al. These results demonstrated successful coating, with uniform top-surface and edge coverage as revealed by energy-dispersive X-ray spectroscopy imaging. Overall, the results indicate that post-release metallization has the potential to improve the operational repeatability of Si-to-Si contact MEMS switches in static and dynamic tests; the findings also point to process refinements to further optimize contact resistance. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications for Semiconductor Industry)

► Show Figures

Figure 1

18 pages, 2983 KB

Open AccessArticle

A Physics-Informed Hybrid Neural Network for High-Precision Temperature Prediction in Semiconductor Process Equipment

by Jiefeng Peng, Liang Hu, Rui Su, Yingnan Shen, Jing Wang, Xin Fu and Xiaodong Ruan

Micromachines 2026, 17(3), 287; https://doi.org/10.3390/mi17030287 - 25 Feb 2026

Viewed by 730

Abstract

High-precision thermal regulation in semiconductor process equipment is critical for product quality, yet it is challenged by actuator transport delays, limited actuator bandwidth due to hardware dynamics, and broadband inlet disturbances in temperature-controlled process fluids. This paper presents a systematic solution integrating architecture optimization with a physics-informed hybrid prediction model to enable effective feedforward compensation. Frequency-domain analysis justifies placing the temperature fluctuation attenuator (TFA) upstream of the heater to filter mid-to-high-frequency disturbances without compromising feedback stability. To address actuation delays, a Physics-CNN-LSTM predictor is developed using a residual learning strategy. This framework employs a mechanism model for baseline estimation and a deep learning network to correct persistent low-frequency residuals caused by unmodeled dynamics. Comparative experiments on industrial data demonstrate that the model achieves a Root Mean Square Error (RMSE) of

3.56 \times 10^{- 5}

K under low-to-mid-frequency inlet disturbances, reducing error by approximately 51.8% compared to a standard LSTM. The model also exhibits strong robustness against disturbance frequency shifts (

R^{2} > 0.996

on unseen data). Furthermore, closed-loop simulations confirm that the proposed feedforward compensation enhances temperature stability in high-precision thermal control. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications for Semiconductor Industry)

► Show Figures

Figure 1

20 pages, 3262 KB

Open AccessArticle

Glass Fall-Offs Detection for Glass Insulated Terminals via a Coarse-to-Fine Machine-Learning Framework

by Weibo Li, Bingxun Zeng, Weibin Li, Nian Cai, Yinghong Zhou, Shuai Zhou and Hao Xia

Micromachines 2026, 17(1), 128; https://doi.org/10.3390/mi17010128 - 19 Jan 2026

Viewed by 1088

Abstract

Glass-insulated terminals (GITs) are widely used in high-reliability microelectronic systems, where glass fall-offs in the sealing region may seriously degrade the reliability of the microelectronic component and further degrade the device reliability. Automatic inspection of such defects is challenging due to strong light reflection, irregular defect appearances, and limited defective samples. To address these issues, a coarse-to-fine machine-learning framework is proposed for glass fall-off detection in GIT images. By exploiting the circular-ring geometric prior of GITs, an adaptive sector partition scheme is introduced to divide the region of interest into sectors. Four categories of sector features, including color statistics, gray-level variations, reflective properties, and gradient distributions, are designed for coarse classification using a gradient boosting decision tree (GBDT). Furthermore, a sector neighbor (SN) feature vector is constructed from adjacent sectors to enhance fine classification. Experiments on real industrial GIT images show that the proposed method outperforms several representative inspection approaches, achieving an average IoU of 96.85%, an F1-score of 0.984, a pixel-level false alarm rate of 0.55%, and a pixel-level missed alarm rate of 35.62% at a practical inspection speed of 32.18 s per image. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications for Semiconductor Industry)

► Show Figures

Figure 1

22 pages, 5744 KB

Open AccessArticle

MCHB-DETR: An Efficient and Lightweight Inspection Framework for Ink Jet Printing Defects in Semiconductor Packaging

by Yibin Chen, Jiayi He, Zhuohao Shi, Yisong Pan and Weicheng Ou

Micromachines 2026, 17(1), 109; https://doi.org/10.3390/mi17010109 - 14 Jan 2026

Viewed by 515

Abstract

In semiconductor packaging and microelectronic manufacturing, inkjet printing technology is widely employed in critical processes such as conductive line fabrication and encapsulant dot deposition. However, dynamic printing defects, such as missing droplets and splashing can severely compromise circuit continuity and device reliability. Traditional inspection methods struggle to detect such subtle and low-contrast defects. To address this challenge, we propose MCHB-DETR, a novel lightweight defect detection framework based on RT-DETR, aimed at improving product yield in inkjet printing for semiconductor packaging. MCHB-DETR features a lightweight backbone with enhanced multi-level feature extraction capabilities and a hybrid encoder designed to improve cross-scale and multi-frequency feature fusion. Experimental results on our inkjet dataset show a 29.1% reduction in parameters and a 36.7% reduction in FLOPs, along with improvements of 3.1% in mAP@50 and 3.5% in mAP@50:95. These results demonstrate its superior detection performance while maintaining efficient inference, highlighting its strong potential for enhancing yield in semiconductor packaging. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications for Semiconductor Industry)

► Show Figures

Figure 1

18 pages, 12671 KB

Open AccessArticle

Numerical Study on Heat Transfer Performance of Turbulence Enhancement Configurations for Galinstan Based Mini-Channel Cooling

by Fajing Li, Junxi Han, Zhifeng Wang, Yi Dai and Peizhu Chen

Micromachines 2026, 17(1), 83; https://doi.org/10.3390/mi17010083 - 7 Jan 2026

Cited by 1 | Viewed by 473

Abstract

The escalating heat flux density and temperature in highly integrated microelectronic devices adversely affect their reliability and service life, making efficient thermal management crucial for stable operation. This study utilizes Galinstan liquid metal as the coolant to investigate the flow and heat transfer performance in microchannel heat sinks incorporating various turbulator configurations. It is revealed that for microchannels featuring expanded regions, turbulators that create highly symmetric flow fields are preferable due to improved flow distribution. The long teardrop-shaped turbulator provides the best heat transfer performance among all the investigated heat transfer enhancement structures. And this turbulator yields a 13.8–25.9% higher enhancement effectiveness compared to other configurations, at the expense of a 28–41% increase in pressure loss. However, the sudden cross-sectional expansion in the expanded region causes a significant reduction in fluid velocity. Consequently, microchannels with expanded regions and turbulators exhibit a higher bottom surface temperature than the original, straight microchannels, leading to an overall deterioration in heat transfer performance. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications for Semiconductor Industry)

► Show Figures

Figure 1

13 pages, 3866 KB

Open AccessArticle

Near-Field Electrospray ZnO Thin Film for Ultraviolet Photodetectors

by Liyun Zhuo, Tao Peng, Jiaxin Jiang and Gaofeng Zheng

Micromachines 2026, 17(1), 69; https://doi.org/10.3390/mi17010069 - 31 Dec 2025

Cited by 2 | Viewed by 600

Abstract

ZnO thin-film ultraviolet photodetectors are widely used in the military, space, environmental protection, medicine, and other fields. Accurate printing of ZnO photoelectric-sensitive films plays a key role in the detection results. Therefore, obtaining printing technology with a simple process and high precision has become a challenge for ZnO photoelectrically sensitive films. By adjusting the distance between the nozzle and the collecting plate, the jet is atomized in a straight line and deposited directly on the collecting plate, which effectively improves the stability and controllability of the jet spraying and deposition processes. ZnO thin films with a uniform distribution of nanoparticles, significantly improved density, and controllable deposition area linewidth were successfully prepared. The effects of different ZnO film structures on the performance of ultraviolet photodetectors were tested. When the ultraviolet light intensity is 500, 1000, and 2500 mW/cm², the

I_{l i g h t}

of the photodetector is 4.62, 9.38, 14.67 mA, The on/off ratio (

I_{l i g h t}

I_{d a r k}

) is 20.7, 42.1, 65.8, implying satisfactory photoelectric performance as well as high stability and repeatability, providing an effective technical means for the precise printing application of micro-nano functional devices. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications for Semiconductor Industry)

► Show Figures

Graphical abstract

14 pages, 2545 KB

Open AccessArticle

Study on the Core-Shell Structure of Gas-Assisted Coaxial Electrospinning Fibers: Implications for Semiconductor Material Design

by Rongguang Zhang, Xuanzhi Zhang, Jianfeng Sun, Shize Huang, Xuan Zhang, Guohuai Lin, Xun Chen, Zhifeng Wang, Jiecai Long and Weiming Shu

Micromachines 2026, 17(1), 20; https://doi.org/10.3390/mi17010020 - 24 Dec 2025

Cited by 1 | Viewed by 595

Abstract

Gas-assisted coaxial electrospinning (GACES), a simple and versatile technique for the large-scale fabrication of coaxial nanofiber membranes, possesses significant industrial potential across advanced manufacturing sectors including semiconductors—particularly for fabricating high-precision dielectric layers, high-uniformity encapsulation materials, and flexible semiconductor substrates requiring tailored core-shell architectures. However, there is still a lack of relevant studies on the effective regulation of the core-shell structures of coaxial fibers based on GACES, which greatly limits the batch preparation and wide application of coaxial fibers. Finite element simulation analysis of the flow field and development of the coaxial jet mechanics model with a gas-driven flow field—two key methodologies in this study—successfully uncovered the influence mechanism of gas-assisted flow fields on the core-shell structures of coaxial nanofibers. By adjusting the gas-assisted flow fields parameters, we reduced the total diameter of coaxial fibers by 47.33% (average fiber diameter: 334.12 ± 16.29 nm → 175.98 ± 1.18 nm), decreased the shell thickness by 72.98%, increased the core-shell ratio by 289% (core-shell ratio: 0.49 → 1.91), and improved the uniformity of the total diameter distribution of coaxial fibers by 30.64%. This study delivers a practical conceptual framework and robust experimental underpinnings for the scalable fabrication of coaxial nanofiber membranes with controllable core-shell structures, thereby promoting their practical application in semiconductor devices such as ultra-thin dielectric layers, precisely structured encapsulation materials, and high-uniformity templates for nanoscale circuit patterning. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications for Semiconductor Industry)

► Show Figures

Figure 1

12 pages, 5636 KB

Open AccessArticle

Enhancement of Piezoelectric Properties in Electrospun PVDF Nanofiber Membranes via In Situ Doping with ZnO or BaTiO₃

by Zhizhao Ouyang, Jinghua Lin, Renhao Rao, Guoqin Huang, Gaofeng Zheng and Changcai Cui

Micromachines 2026, 17(1), 12; https://doi.org/10.3390/mi17010012 - 23 Dec 2025

Viewed by 863

Abstract

High-performance piezoelectric poly(vinylidene fluoride) (PVDF) has great application potential in the field of microsensors, but achieving efficient polarization remains a challenge. Here, the in situ doping electrospinning technique is employed to enhance the piezoelectric properties by introducing a single dose of zinc oxide (ZnO) or barium titanate (BaTiO₃,BTO) dopants. The effects of key processing parameters on the morphology of nanofiber membranes were systematically investigated. In addition, the influence of zinc oxide (ZnO) or barium titanate (BTO) dopant concentrations on the piezoelectric properties of PVDF was examined. The microstructure, electrical performance, and β-phase content of the composite membranes were characterized. Results indicate that the composite film with a doping formulation of 16 wt% PVDF and 10 wt% ZnO exhibits optimal overall performance: the β-phase content of PVDF reaches 52.8%, and the output voltage reaches 1.5 V, which is 2.5 times higher than that of the undoped PVDF nanofiber membranes. This study provides an effective doping strategy for the fabrication of high-performance piezoelectric nanofiber membranes. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications for Semiconductor Industry)

► Show Figures

Journal Menu

Journal Browser

Emerging Technologies and Applications for Semiconductor Industry

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (14 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI