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15th Anniversary of Journal of Low Power Electronics and Applications

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A special issue of Journal of Low Power Electronics and Applications (ISSN 2079-9268).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 7299

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Editor

Dr. Davide Bertozzi

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Guest Editor

Reader in Advanced Processing Technologies, Department of Computer Science, University of Manchester, Manchester M13 9PL, UK
Interests: neuromorphic computing; interconnection networks; fault tolerant computing; emerging technologies; cloud-edge continuum

Special Issue Information

Dear Colleagues,

Since it launched in 2011, Journal of Low Power Electronics and Applications has provided readers with high-quality content edited by active researchers in low power electronics and design through a model of sustainable open access. We would like to sincerely thank our readers, authors, anonymous peer reviewers, editors, any individuals who work for the journal and all those who have contributed time and effort throughout the years for their interest and commitment.

To celebrate the significant milestone of the 15th Anniversary, we are delighted to launch the Special Issue entitled “15th Anniversary of Journal of Low Power Electronics and Applications”. It is our pleasure to invite you to contribute research articles and communications, as well as comprehensive review articles on a current, trending topic from the field of low power devices, design, architecture and process technologies.

Dr. Davide Bertozzi
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Low Power Electronics and Applications is an international peer-reviewed open access quarterly 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 1800 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.

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Published Papers (7 papers)

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Research

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15 pages, 13032 KB

Open AccessArticle

Low-Power IGZO TFTs with Improved Positive Bias Stability via Atomic Layer Deposition-Based H₂O Treatment

by Kai-Ting Huang, You-Wen Fan, Jung-Yi Lin, Chien-Lung Chen, Yen-Chih Yeh, Yu-Chen Ou, Li-Chen Lin, Yu-Hsien Lin, Guang-Li Luo, Yung-Chun Wu and Fu-Ju Hou

J. Low Power Electron. Appl. 2026, 16(3), 31; https://doi.org/10.3390/jlpea16030031 - 10 Aug 2026

Viewed by 301

Abstract

In this work, a plasma-free atomic layer deposition (ALD)-based H₂O post-treatment method is proposed to precisely modulate hydrogen-related (H-related) traps in indium gallium zinc oxide (IGZO) thin-film transistors (TFTs) by the number of H₂O treatment cycles. Under the optimized condition, the scaled device with a channel length of 70 nm exhibits a near-ideal subthreshold swing of 62.9 mV/dec, a low threshold voltage (V_TH) of 0.18 V, an acceptable static leakage current, and a high drive current of 3.29 μA/μm at an overdrive voltage and drain voltage of 1 V. In addition, the treated device shows only a 13 mV of V_TH shift after 1000 s positive bias stress (PBS), corresponding to a 94% improvement compared with the pristine device. These improvements are attributed to the introduction of two different polarities of hydrogen-related traps after H₂O treatment. Furthermore, the influence of H-related traps on bias stability and the mechanisms responsible for V_TH shift are systematically clarified. These results establish that an optimized hydrogen incorporation window that maximizes the beneficial effects while balancing severe hydrogen-induced degradation caused by excessive hydrogen incorporation. Consequently, scaled IGZO TFTs with fast switching, low-power operation, high performance, and high reliability can be achieved, providing strong potential for back-end-of-line (BEOL)-compatible electronics and monolithic three-dimensional integrated applications. Full article

(This article belongs to the Special Issue 15th Anniversary of Journal of Low Power Electronics and Applications)

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20 pages, 548 KB

Open AccessArticle

Sensorless Current Estimation in Piezoelectric Energy Harvesting Networks Using a Takagi–Sugeno Fuzzy System

by Joel Artemio Morales-Viscaya, Martin Moreno, Alberto Traslosheros-Michel and H. J. Vergara-Hernández

J. Low Power Electron. Appl. 2026, 16(3), 30; https://doi.org/10.3390/jlpea16030030 - 7 Aug 2026

Viewed by 252

Abstract

This paper proposes a sensorless current estimation method for piezoelectric energy harvesting (PEH) systems using a first-order Takagi–Sugeno fuzzy system. Unlike invasive current sensing, the proposed estimator uses only non-invasive measurements: output voltage

V_{O}

, its derivative

{\dot{V}}_{O}

, and [...] Read more.

V_{O}

, its derivative

{\dot{V}}_{O}

, and load resistance

R_{L}

. The fuzzy rules are initialized directly from the physical equivalent circuit parameters and trained via the ANFIS on a large-scale dataset (78 million samples). The proposed model achieves a mean coefficient of determination

R^{2} = 0.9999

(95% CI: [0.99989, 0.99991]), root mean square error

RMSE = 3.12 \times 10^{- 8}

A, mean absolute percentage error MAPE = 2.51% (95% CI: [1.98, 3.04]%), and fitness FIT = 98.98%—outperforming multiple linear regression

(R^{2} = 0.9738

and MAPE = 116.25%) and a shallow neural network with 211 parameters

(R^{2} = 0.9991

and MAPE = 13.85%) despite having only 170 trainable parameters. Unlike black-box neural networks, the fuzzy model provides interpretable rules whose consequent parameters map directly to physical quantities (effective capacitance

C_{p}^{(eff)}

and leakage conductance

1 / R_{p}^{(eff)}

). The low computational footprint (170 parameters, <5 μs inference, and ≈1.4 kB of memory) makes it suitable for real-time deployment on low-power microcontrollers. These results demonstrate the viability of the proposed approach under controlled laboratory conditions for the single, series, and parallel PEH configurations considered. This work establishes that physically informed fuzzy modeling is a viable, interpretable, and efficient alternative to deep learning for sensorless monitoring in low-power energy harvesting systems. Full article

(This article belongs to the Special Issue 15th Anniversary of Journal of Low Power Electronics and Applications)

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28 pages, 5334 KB

Open AccessArticle

Can Federated Learning Go Green? EcoFL: A System-Level Energy-Aware Benchmark for IoT Edge Intelligence

by Tymoteusz Miller and Irmina Durlik

J. Low Power Electron. Appl. 2026, 16(3), 24; https://doi.org/10.3390/jlpea16030024 - 8 Jul 2026

Viewed by 420

Abstract

The proliferation of Internet of Things (IoT) devices operating at the network edge has created unprecedented demand for distributed machine learning capable of functioning under severe resource constraints. Federated learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative model training across distributed nodes; however, its application to energy-constrained edge environments remains insufficiently characterized at the system level, particularly with respect to reproducible evaluation of resource consumption and communication efficiency. In this paper, we present EcoFL (Energy-Conscious Federated Learning), a modular, energy-aware benchmarking and orchestration framework for systematic evaluation of lightweight machine learning models under emulated edge hardware constraints. Rather than proposing a new federated optimization algorithm, EcoFL extends a standard FedAvg-based training pipeline with three principal components: (i) an energy-aware communication scheduler that dynamically adapts aggregation rounds and client participation based on per-node resource availability; (ii) a comprehensive system-level profiling pipeline capturing CPU utilization, RAM consumption, inference latency, communication overhead, and estimated computational energy consumption per training round; and (iii) a reproducible benchmarking methodology enabling fair comparison of centralized, standard federated (FedAvg), and energy-aware federated configurations. We evaluate five lightweight model families—Logistic Regression, Random Forest, XGBoost, Multilayer Perceptron, and Isolation Forest—under emulated Raspberry Pi 4 hardware constraints using an anomaly detection task on synthetic IoT sensor telemetry (50,000 samples, 12 features, Dirichlet non-IID partitioning). Experimental results across five independent seeds show that, within the evaluated benchmark setting, EcoFL reduces estimated federated training energy by 79.9–92.9% (mean 84.4%) relative to standard FedAvg through adaptive round termination (4–7 rounds versus 20 fixed rounds), while showing no statistically significant F1-score degradation for four of the five evaluated model families under the tested seed regime. Notably, EcoFL achieves a higher F1-score than FedAvg for Random Forest (+0.052), which we attribute to reduced overfitting resulting from earlier convergence under non-IID data distributions. The full EcoFL framework is released as open-source software to promote reproducibility in energy-aware federated learning research and to facilitate systematic investigation of the trade-offs between predictive performance, resource utilization, and communication overhead in resource-constrained edge environments. Full article

(This article belongs to the Special Issue 15th Anniversary of Journal of Low Power Electronics and Applications)

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17 pages, 2015 KB

Open AccessArticle

Efficient Battery State of Health Estimation Using Lightweight ML Models Based on Limited Voltage Measurements

by Mohammad Okour, Mohannad Alkhalil, Mutaz Al Fayad, Juhyun Bak, Kevin R. James, Sulaiman Mohaidat, Xiaoqi Liu, Fadi Alsaleem, Michael Hempel, Hamid Sharif-Kashani and Mahmoud Alahmad

J. Low Power Electron. Appl. 2026, 16(2), 16; https://doi.org/10.3390/jlpea16020016 - 21 Apr 2026

Viewed by 1392

Abstract

Accurate estimation of lithium-ion battery State of Health (SoH) is critical for emerging applications such as reconfigurable battery systems. Although data-driven machine learning methods are promising, they often rely on costly, time-intensive aging experiments and extensive feature engineering. This work proposes a lightweight SoH-prediction framework validated on both physics-informed synthetic aging data and the NASA battery aging dataset. We evaluated Random Forest (RF) and Feedforward Neural Network (FNN) models that use only a limited number of samples from an early segment of the raw discharge voltage curve as input. Results show that RF consistently outperforms FNN across input sizes in deterministic or noise-free environments, achieving an RMSE of 0.07% SoH using just 5 voltage samples. In inherently stochastic experimental data, however, FNN can achieve an RMSE 50% lower than RF (1.28 vs. 2.87), but requires 37× more mathematical operations per inference. These findings emphasize the predictive value of the early-discharge-voltage region and demonstrate that compact, low-feature-complexity models can deliver accurate SoH estimates. Overall, the approach supports a goal of combining informed synthetic data with limited real measurements to build robust, scalable SoH predictors, reducing dependence on labor-intensive degradation testing and feature-heavy pipelines. Full article

(This article belongs to the Special Issue 15th Anniversary of Journal of Low Power Electronics and Applications)

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16 pages, 1361 KB

Open AccessArticle

RF/mm-Wave Frequency Doublers in CMOS Technology

by Manfredi Caruso, Andrea Ballo, Minoo Eghtesadi and Egidio Ragonese

J. Low Power Electron. Appl. 2026, 16(2), 14; https://doi.org/10.3390/jlpea16020014 - 13 Apr 2026

Viewed by 1505

Abstract

This paper provides a comprehensive analysis of active frequency doubler architectures adopted for efficient generation of millimeter-wave (mm-wave) signals. The operational principles of each topology are explained to address a thorough comparison based on essential performance metrics such as conversion gain, power efficiency, and spectral purity. The review covers several topologies from the standard push–push (PP) doubler to its power-efficient evolution, the complementary push–push (CPP) doubler. Furthermore, this paper focuses on more recent and advanced topologies, including the complementary common gate capacitive cross-coupled (CCGCCC) doubler. Finally, this work proposes and evaluates an improved version of the CCCGCC doubler, offering insights into the state of the art and future directions in mm-wave frequency multiplication. Full article

(This article belongs to the Special Issue 15th Anniversary of Journal of Low Power Electronics and Applications)

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21 pages, 9981 KB

Open AccessArticle

Forward-Flyback Resonant Topology with Edge AI for MPPT Control in Solar Power Generation

by Juan Cruz-Cozar, Javier Mendez, Miguel Molina, Jorge Perez-Martinez, Alberto Martin-Martin, Noel Rodriguez and Diego P. Morales

J. Low Power Electron. Appl. 2026, 16(2), 13; https://doi.org/10.3390/jlpea16020013 - 12 Apr 2026

Viewed by 1530

Abstract

Distributed energy systems open up a vast field of research in power electronics. Local solar power generation requires DC-DC converters that adapt the energy generated by the panels to on-site distribution buses. In addition, the control of the power converter to obtain the maximum possible energy from the solar source is crucial for the correct deployment of these distributed grids. In this work, system-level solutions are proposed for this application as follows: On the one hand, the use of novel resonant forward-flyback converters allows for a higher energy density than that of a conventional flyback and more relaxed withstand voltages on the switching elements. On the other hand, the implementation of maximum power point tracking algorithms for solar energy using Edge AI enables the deployment of algorithms that maximize the energy obtained locally. These improvements are shown by means of a prototype demonstrator, using cutting-edge microcontrollers and the implementation of a DC-DC power converter based on the proposed topology. Full article

(This article belongs to the Special Issue 15th Anniversary of Journal of Low Power Electronics and Applications)

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Review

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33 pages, 1072 KB

Open AccessReview

3D Integrated DNN Accelerators: Recent Trends and Future Prospects

by Abrar Abdurrob, Aristotelis Tsekouras, Evangelos Tzouvaras, Vasilis F. Pavlidis and Emre Salman

J. Low Power Electron. Appl. 2026, 16(2), 21; https://doi.org/10.3390/jlpea16020021 - 18 Jun 2026

Viewed by 1000

Abstract

The rapid growth of Deep Neural Networks (DNNs) has led to the development of application-specific DNN accelerators. Conventional 2D von Neumann architectures suffer from memory bandwidth limitations between the memory and the processing core. 3D DNN accelerators have emerged as a promising solution by leveraging 3D integration to enable near-memory logic or in-memory computation. By shifting computation closer to memory, these accelerators significantly reduce data movement and therefore latency, resulting in more energy-efficient operations. Monolithic 3D (M3D) integration, in particular, enables high-bandwidth systems by utilizing high-density monolithic inter-tier vias (MIVs). This paper provides a critical review of recent advances in 3D DNN accelerators that combine near-memory and compute-in-memory with various 3D technologies, offering a useful discussion and future prospects of the available technologies and architectures that have advanced the performance of DNN accelerators. Particular attention is devoted to accelerators for emerging transformer-based large language model (LLM) networks due to the higher memory demands. Thermal-aware design techniques of 3D DNN accelerators are also discussed as a means to address the fundamental challenge of heat dissipation. A detailed review is finally conducted on package-level constraints, considering signal integrity, power delivery, and thermo-mechanical reliability. Full article

(This article belongs to the Special Issue 15th Anniversary of Journal of Low Power Electronics and Applications)

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