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With the ever-growing complexity of models in the field of remote sensing (RS), there is an increasing demand for solutions that balance model accuracy with computational efficiency. Knowledge distillation (KD) has emerged as a powerful tool to meet this need, enabling the transfer of knowledge from large, complex models to smaller, more efficient ones without significant loss in performance. This review article provides an extensive examination of KD and its innovative applications in RS. KD, a technique developed to transfer knowledge from a complex, often cumbersome model (teacher) to a more compact and efficient model (student), has seen significant evolution and application across various domains. Initially, we introduce the fundamental concepts and historical progression of KD methods. The advantages of employing KD are highlighted, particularly in terms of model compression, enhanced computational efficiency, and improved performance, which are pivotal for practical deployments in RS scenarios. The article provides a comprehensive taxonomy of KD techniques, where each category is critically analyzed to demonstrate the breadth and depth of the alternative options, and illustrates specific case studies that showcase the practical implementation of KD methods in RS tasks, such as instance segmentation and object detection. Further, the review discusses the challenges and limitations of KD in RS, including practical constraints and prospective future directions, providing a comprehensive overview for researchers and practitioners in the field of RS. Through this organization, the paper not only elucidates the current state of research in KD but also sets the stage for future research opportunities, thereby contributing significantly to both academic research and real-world applications., Comment: 50 pages, 11 figures and 9 tables

Many incurable diseases prevalent across global societies stem from various influences, including lifestyle choices, economic conditions, social factors, and genetics. Research predominantly focuses on these diseases due to their widespread nature, aiming to decrease mortality, enhance treatment options, and improve healthcare standards. Among these, kidney disease stands out as a particularly severe condition affecting men and women worldwide. Nonetheless, there is a pressing need for continued research into innovative, early diagnostic methods to develop more effective treatments for such diseases. Recently, automatic diagnosis of Kidney Cancer has become an important challenge especially when using deep learning (DL) due to the importance of training medical datasets, which in most cases are difficult and expensive to obtain. Furthermore, in most cases, algorithms require data from the same domain and a powerful computer with efficient storage capacity. To overcome this issue, a new type of learning known as transfer learning (TL) has been proposed that can produce impressive results based on other different pre-trained data. This paper presents, to the best of the authors' knowledge, the first comprehensive survey of DL-based TL frameworks for kidney cancer diagnosis. This is a strong contribution to help researchers understand the current challenges and perspectives of this topic. Hence, the main limitations and advantages of each framework are identified and detailed critical analyses are provided. Looking ahead, the article identifies promising directions for future research. Moving on, the discussion is concluded by reflecting on the pivotal role of TL in the development of precision medicine and its effects on clinical practice and research in oncology., Comment: 32 pages, 8 figures and 8 tables

The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of deep learning (DL). However, the latter faces various issues, including the lack of data or annotated data, the existence of a significant gap between training data and test data, and the requirement for high computational resources. To that end, deep transfer learning (DTL), which decreases dependency and costs by utilizing knowledge gained from a source data/task in training a target data/task, has been widely investigated. Numerous DTL frameworks have been suggested for aligning point clouds obtained from several scans of the same scene. Additionally, DA, which is a subset of DTL, has been modified to enhance the point cloud data's quality by dealing with noise and missing points. Ultimately, fine-tuning and DA approaches have demonstrated their effectiveness in addressing the distinct difficulties inherent in point cloud data. This paper presents the first review shedding light on this aspect. it provides a comprehensive overview of the latest techniques for understanding 3DPC using DTL and domain adaptation (DA). Accordingly, DTL's background is first presented along with the datasets and evaluation metrics. A well-defined taxonomy is introduced, and detailed comparisons are presented, considering different aspects such as different knowledge transfer strategies, and performance. The paper covers various applications, such as 3DPC object detection, semantic labeling, segmentation, classification, registration, downsampling/upsampling, and denoising. Furthermore, the article discusses the advantages and limitations of the presented frameworks, identifies open challenges, and suggests potential research directions., Comment: 55 pages, 9 tables, and 15 figures

Machine learning has revolutionized the field of agricultural science, particularly in the early detection and management of plant diseases, which are crucial for maintaining crop health and productivity. Leveraging advanced algorithms and imaging technologies, researchers are now able to identify and classify plant diseases with unprecedented accuracy and speed. Effective management of tomato diseases is crucial for enhancing agricultural productivity. The development and application of tomato disease classification methods are central to this objective. This paper introduces a cutting-edge technique for the detection and classification of tomato leaf diseases, utilizing insights from the latest pre-trained Convolutional Neural Network (CNN) models. We propose a sophisticated approach within the domain of tensor subspace learning, known as Higher-Order Whitened Singular Value Decomposition (HOWSVD), designed to boost the discriminatory power of the system. Our approach to Tensor Subspace Learning is methodically executed in two phases, beginning with HOWSVD and culminating in Multilinear Discriminant Analysis (MDA). The efficacy of this innovative method was rigorously tested through comprehensive experiments on two distinct datasets, namely PlantVillage and the Taiwan dataset. The findings reveal that HOWSVD-MDA outperforms existing methods, underscoring its capability to markedly enhance the precision and dependability of diagnosing tomato leaf diseases. For instance, up to 98.36\% and 89.39\% accuracy scores have been achieved under PlantVillage and the Taiwan datasets, respectively., Comment: 17 pages, 9 figures and 8 tables

The growing interest in developing smart diagnostic systems to help medical experts process extensive data for treating incurable diseases has been notable. In particular, the challenge of identifying thyroid cancer (TC) has seen progress with the use of machine learning (ML) and big data analysis, incorporating transformers to evaluate TC prognosis and determine the risk of malignancy in individuals. This review article presents a summary of various studies on AIbased approaches, especially those employing transformers, for diagnosing TC. It introduces a new categorization system for these methods based on artifcial intelligence (AI) algorithms, the goals of the framework, and the computing environments used. Additionally, it scrutinizes and contrasts the available TC datasets by their features. The paper highlights the importance of AI instruments in aiding the diagnosis and treatment of TC through supervised, unsupervised, or mixed approaches, with a special focus on the ongoing importance of transformers in medical diagnostics and disease management. It further discusses the progress made and the continuing obstacles in this area. Lastly, it explores future directions and focuses within this research feld.

Due to their narrow band gap and compatibility with silicon processing, germanium-tin (Ge$_{1-x}$Sn$_x$) alloys are a versatile platform for scalable integrated mid-infrared photonics. These semiconductors are typically grown on silicon wafers using Ge as an interlayer. However, the large lattice mismatch in this heteroepitaxy protocol leads to the build-up of compressive strain in the grown layers. This compressive strain limits the material quality and its thermal stability besides expanding the band gap, thereby increasing the Sn content needed to cover a broader range in the mid-infrared. Released Ge$_{1-x}$Sn$_x$ membranes provide an effective way to mitigate these harmful effects of the epitaxial strain and control the band gap energy while enabling the hybrid integration onto different substrates. With this perspective, herein strain-relaxed Ge$_{0.89}$Sn$_{0.11}$ membranes are fabricated and subsequently transfer-printed with metal contacts to create multiple photodetectors in a single transfer step. The resulting photodetectors exhibit an extended photodetection cutoff reaching a wavelength of $3.1 \,\mu$m, coupled with a significant reduction in the dark current of two orders of magnitude as compared to as-grown photoconductive devices. The latter yields a reduced cutoff of $2.8 \,\mu$m due to the inherent compressive strain. Furthermore, the impact of chemical treatment and annealing on the device performance was also investigated showing a further reduction in the dark current. The demonstrated transfer printing, along with the use of an adhesive layer, would allow the transfer of multiple GeSn membranes onto virtually any substrate. This approach paves the way for scalable fabrication of hybrid optoelectronic devices leveraging the tunable band gap of Ge$_{1-x}$Sn$_x$ in the mid-wave infrared range.

There is an increasing need for silicon-compatible high bandwidth extended-short wave infrared (e-SWIR) photodetectors (PDs) to implement cost-effective and scalable optoelectronic devices. These systems are quintessential to address several technological bottlenecks in detection and ranging, surveillance, ultrafast spectroscopy, and imaging. In fact, current e-SWIR high bandwidth PDs are predominantly made of III-V compound semiconductors and thus are costly and suffer a limited integration on silicon besides a low responsivity at wavelengths exceeding $2.3 \,\mu$m. To circumvent these challenges, Ge$_{1-x}$Sn$_{x}$ semiconductors have been proposed as building blocks for silicon-integrated high-speed e-SWIR devices. Herein, this study demonstrates a vertical all-GeSn PIN PDs consisting of p-Ge$_{0.92}$Sn$_{0.08}$/i-Ge$_{0.91}$Sn$_{0.09}$/n-Ge$_{0.89}$Sn$_{0.11}$ and p-Ge$_{0.91}$Sn$_{0.09}$/i-Ge$_{0.88}$Sn$_{0.12}$/n-Ge$_{0.87}$Sn$_{0.13}$ heterostructures grown on silicon following a step-graded temperature-controlled epitaxy protocol. The performance of these PDs was investigated as a function of the device diameter in the $10-30 \,\mu$m range. The developed PD devices yield a high bandwidth of 12.4 GHz at a bias of 5V for a device diameter of $10 \,\mu$m. Moreover, these devices show a high responsivity of 0.24 A/W, a low noise, and a $2.8 \,\mu$m cutoff wavelength thus covering the whole e-SWIR range.

The challenge of kinship verification from facial images represents a cutting-edge and formidable frontier in the realms of pattern recognition and computer vision. This area of study holds a myriad of potential applications, spanning from image annotation and forensic analysis to social media research. Our research stands out by integrating a preprocessing method named Multiscale Retinex (MSR), which elevates image quality and amplifies contrast, ultimately bolstering the end results. Strategically, our methodology capitalizes on the harmonious blend of deep and shallow texture descriptors, merging them proficiently at the score level through the Logistic Regression (LR) method. To elucidate, we employ the Local Phase Quantization (LPQ) descriptor to extract shallow texture characteristics. For deep feature extraction, we turn to the prowess of the VGG16 model, which is pre-trained on a convolutional neural network (CNN). The robustness and efficacy of our method have been put to the test through meticulous experiments on three rigorous kinship datasets, namely: Cornell Kin Face, UB Kin Face, and TS Kin Face.

This research paper delves into the field of quadrotor dynamics, which are famous by their nonlinearity, under-actuation, and multivariable nature. Due to the critical need for precise modeling and control in this context we explore the capabilities of NARX (Nonlinear AutoRegressive with eXogenous inputs) Neural Networks (NN). These networks are employed for comprehensive and accurate modeling of quadrotor behaviors, take advantage of their ability to capture the hided dynamics. Our research encompasses a rigorous experimental setup, including the use of PRBS (Pseudo-random binary sequence) signals for excitation, to validate the efficacy of NARX-NN in predicting and controlling quadrotor dynamics. The results reveal exceptional accuracy, with fit percentages exceeding 99% on both estimation and validation data. Moreover, we identified the quadrotor dynamics using different NARX NN structures, including the NARX model with a sigmoid NN, NARX feedforward NN, and cascade NN. In summary, our study positions NARX-NN as a transformative tool for quadrotor applications, ranging from autonomous navigation to aerial robotics, thanks to their accurate and comprehensive modeling capabilities., Comment: 6 pages, 10 figures, and 0 table

Pipelines, vital for fluid transport, pose an important yet challenging inspection task, particularly in small, flexible biological systems, that robots have yet to master. In this study, we explored the development of an innovative robot inspired by the ovipositor of parasitic wasps to navigate and inspect pipelines. The robot features a flexible locomotion system that adapts to different tube sizes and shapes through a mechanical inflation technique. The flexible locomotion system employs a reciprocating motion, in which groups of three sliders extend and retract in a cyclic fashion. In a proof-of-principle experiment, the robot locomotion efficiency demonstrated positive linear correlation (r=0.6434) with the diameter ratio (ratio of robot diameter to tube diameter). The robot showcased a remarkable ability to traverse tubes of different sizes, shapes and payloads with an average of (70%) locomotion efficiency across all testing conditions, at varying diameter ratios (0.7-1.5). Furthermore, the mechanical inflation mechanism displayed substantial load-carrying capacity, producing considerable holding force of (13 N), equivalent to carrying a payload of approximately (5.8 Kg) inclusive the robot weight. This novel soft robotic system shows promise for inspection and navigation within tubular confined spaces, particularly in scenarios requiring adaptability to different tube shapes, sizes, and load-carrying capacities. This novel design serves as a foundation for a new class of pipeline inspection robots that exhibit versatility across various pipeline environments, potentially including biological systems., Comment: Accepted paper for RoboSoft 2024

We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services. Our framework for automatically measuring harms from LLMs builds on existing technical and sociotechnical expertise and leverages the capabilities of state-of-the-art LLMs, such as GPT-4. We use this framework to run through several case studies investigating how different LLMs may violate a range of RAI-related principles. The framework may be employed alongside domain-specific sociotechnical expertise to create measurements for new harm areas in the future. By implementing this framework, we aim to enable more advanced harm measurement efforts and further the responsible use of LLMs., Comment: This is a living document

Cost-effective mid-wave infrared (MWIR) optoelectronic devices are of utmost importance to a plethora of applications such as night vision, thermal sensing, autonomous vehicles, free-space communication, and spectroscopy. To this end, leveraging the ubiquitous silicon-based processing has emerged as a powerful strategy that can be accomplished through the use of group IV germanium-tin (GeSn) alloys. Indeed, due to their compatibility with silicon and their tunable bandgap energy covering the entire MWIR range, GeSn semiconductors are frontrunner platforms for compact and scalable MWIR technologies. However, the GeSn large lattice parameter has been a major hurdle limiting the quality of GeSn epitaxy on silicon wafers. Herein, it is shown that sub-20 nm Ge nanowires (NWs) provide effective compliant substrates to grow Ge$_{1-x}$Sn$_{x}$ alloys with a composition uniformity over several micrometers with a very limited build-up of the compressive strain. Ge/Ge$_{1-x}$Sn$_{x}$ core/shell NWs with Sn content spanning the 6 to 18 at.$\%$ range are demonstrated and integrated in photoconductive devices exhibiting a high signal-to-noise ratio at room temperature and a tunable cutoff wavelength covering the 2.0 $\mu$m to 3.9 $\mu$m range. Additionally, the processed NW-based detectors were used in uncooled imagers enabling the acquisition of high-quality images under both broadband and laser illuminations without a lock-in technique.

Silicon-compatible short- and mid-wave infrared emitters are highly sought-after for on-chip monolithic integration of electronic and photonic circuits to serve a myriad of applications in sensing and communication. To address this longstanding challenge, GeSn semiconductors have been proposed as versatile building blocks for silicon-integrated optoelectronic devices. In this regard, this work demonstrates light-emitting diodes (LEDs) consisting of a vertical PIN double heterostructure p-Ge$_{0.94}$Sn$_{0.06}$/i-Ge$_{0.91}$Sn$_{0.09}$/n-Ge$_{0.95}$Sn$_{0.05}$ grown epitaxially on a silicon wafer using germanium interlayer and multiple GeSn buffer layers. The emission from these GeSn LEDs at variable diameters in the 40-120 $\mu$m range is investigated under both DC and AC operation modes. The fabricated LEDs exhibit a room temperature emission in the extended short-wave range centered around 2.5 $\mu$m under an injected current density as low as 45 A/cm$^2$. By comparing the photoluminescence and electroluminescence signals, it is demonstrated that the LED emission wavelength is not affected by the device fabrication process or heating during the LED operation. Moreover, the measured optical power was found to increase monotonically as the duty cycle increases indicating that the DC operation yields the highest achievable optical power. The LED emission profile and bandwidth are also presented and discussed.

There has been a growing interest in creating intelligent diagnostic systems to assist medical professionals in analyzing and processing big data for the treatment of incurable diseases. One of the key challenges in this field is detecting thyroid cancer, where advancements have been made using machine learning (ML) and big data analytics to evaluate thyroid cancer prognosis and determine a patient's risk of malignancy. This review paper summarizes a large collection of articles related to artificial intelligence (AI)-based techniques used in the diagnosis of thyroid cancer. Accordingly, a new classification was introduced to classify these techniques based on the AI algorithms used, the purpose of the framework, and the computing platforms used. Additionally, this study compares existing thyroid cancer datasets based on their features. The focus of this study is on how AI-based tools can support the diagnosis and treatment of thyroid cancer, through supervised, unsupervised, or hybrid techniques. It also highlights the progress made and the unresolved challenges in this field. Finally, the future trends and areas of focus in this field are discussed., Comment: 30 pages, 16 figures and 10 tables

Computer Vision (CV) is playing a significant role in transforming society by utilizing machine learning (ML) tools for a wide range of tasks. However, the need for large-scale datasets to train ML models creates challenges for centralized ML algorithms. The massive computation loads required for processing and the potential privacy risks associated with storing and processing data on central cloud servers put these algorithms under severe strain. To address these issues, federated learning (FL) has emerged as a promising solution, allowing privacy preservation by training models locally and exchanging them to improve overall performance. Additionally, the computational load is distributed across multiple clients, reducing the burden on central servers. This paper presents, to the best of the authors' knowledge, the first review discussing recent advancements of FL in CV applications, comparing them to conventional centralized training paradigms. It provides an overview of current FL applications in various CV tasks, emphasizing the advantages of FL and the challenges of implementing it in CV. To facilitate this, the paper proposes a taxonomy of FL techniques in CV, outlining their applications and security threats. It also discusses privacy concerns related to implementing blockchain in FL schemes for CV tasks and summarizes existing privacy preservation methods. Moving on, the paper identifies open research challenges and potential future research directions to further exploit the potential of FL and blockchain in CV applications., Comment: 30 pages, 13 figures and 4 figures

Chat Generative Pre-trained Transformer (ChatGPT) has gained significant interest and attention since its launch in November 2022. It has shown impressive performance in various domains, including passing exams and creative writing. However, challenges and concerns related to biases and trust persist. In this work, we present a comprehensive review of over 100 Scopus-indexed publications on ChatGPT, aiming to provide a taxonomy of ChatGPT research and explore its applications. We critically analyze the existing literature, identifying common approaches employed in the studies. Additionally, we investigate diverse application areas where ChatGPT has found utility, such as healthcare, marketing and financial services, software engineering, academic and scientific writing, research and education, environmental science, and natural language processing. Through examining these applications, we gain valuable insights into the potential of ChatGPT in addressing real-world challenges. We also discuss crucial issues related to ChatGPT, including biases and trustworthiness, emphasizing the need for further research and development in these areas. Furthermore, we identify potential future directions for ChatGPT research, proposing solutions to current challenges and speculating on expected advancements. By fully leveraging the capabilities of ChatGPT, we can unlock its potential across various domains, leading to advancements in conversational AI and transformative impacts in society., Comment: 31 pages. 8 figures and 3 tables

The growing interest in unmanned aerial vehicles (UAVs) from both scientific and industrial sectors has attracted a wave of new researchers and substantial investments in this expansive field. However, due to the wide range of topics and subdomains within UAV research, newcomers may find themselves overwhelmed by the numerous options available. It is therefore crucial for those involved in UAV research to recognize its interdisciplinary nature and its connections with other disciplines. This paper presents a comprehensive overview of the UAV field, highlighting recent trends and advancements. Drawing on recent literature reviews and surveys, the review begins by classifying UAVs based on their flight characteristics. It then provides an overview of current research trends in UAVs, utilizing data from the Scopus database to quantify the number of scientific documents associated with each research direction and their interconnections. The paper also explores potential areas for further development in UAVs, including communication, artificial intelligence, remote sensing, miniaturization, swarming and cooperative control, and transformability. Additionally, it discusses the development of aircraft control, commonly used control techniques, and appropriate control algorithms in UAV research. Furthermore, the paper addresses the general hardware and software architecture of UAVs, their applications, and the key issues associated with them. It also provides an overview of current open-source software and hardware projects in the UAV field. By presenting a comprehensive view of the UAV field, this paper aims to enhance understanding of this rapidly evolving and highly interdisciplinary area of research., Comment: 32 pages, 4 figures, and 5 tables

Solar Photovoltaic (PV) is increasingly being used to address the global concern of energy security. However, hot spot and snail trails in PV modules caused mostly by crakes reduce their efficiency and power capacity. This article presents a groundbreaking methodology for automatically identifying and analyzing anomalies like hot spots and snail trails in Solar Photovoltaic (PV) modules, leveraging unsupervised sensing algorithms and 3D Augmented Reality (AR) visualization. By transforming the traditional methods of diagnosis and repair, our approach not only enhances efficiency but also substantially cuts down the cost of PV system maintenance. Validated through computer simulations and real-world image datasets, the proposed framework accurately identifies dirty regions, emphasizing the critical role of regular maintenance in optimizing the power capacity of solar PV modules. Our immediate objective is to leverage drone technology for real-time, automatic solar panel detection, significantly boosting the efficacy of PV maintenance. The proposed methodology could revolutionize solar PV maintenance, enabling swift, precise anomaly detection without human intervention. This could result in significant cost savings, heightened energy production, and improved overall performance of solar PV systems. Moreover, the novel combination of unsupervised sensing algorithms with 3D AR visualization heralds new opportunities for further research and development in solar PV maintenance., Comment: 13 pages, 8 figures, 1 table

Person re-identification (PRe-ID) is a crucial task in security, surveillance, and retail analysis, which involves identifying an individual across multiple cameras and views. However, it is a challenging task due to changes in illumination, background, and viewpoint. Efficient feature extraction and metric learning algorithms are essential for a successful PRe-ID system. This paper proposes a novel approach for PRe-ID, which combines a Convolutional Neural Network (CNN) based feature extraction method with Cross-view Quadratic Discriminant Analysis (XQDA) for metric learning. Additionally, a matching algorithm that employs Mahalanobis distance and a score normalization process to address inconsistencies between camera scores is implemented. The proposed approach is tested on four challenging datasets, including VIPeR, GRID, CUHK01, and PRID450S, and promising results are obtained. For example, without normalization, the rank-20 rate accuracies of the GRID, CUHK01, VIPeR and PRID450S datasets were 61.92%, 83.90%, 92.03%, 96.22%; however, after score normalization, they have increased to 64.64%, 89.30%, 92.78%, and 98.76%, respectively. Accordingly, the promising results on four challenging datasets indicate the effectiveness of the proposed approach., Comment: 5 pages, 6 figures and 2 tables

Compound semiconductors have been the predominant building blocks for the current mid-infrared thermophotovoltaic devices relevant to sub-2000 K heat conversion and power beaming. However, the prohibitively high cost associated with these technologies limits their broad adoption. Herein, to alleviate this challenge we introduce an all-group IV mid-infrared cell consisting of GeSn alloy directly on a silicon wafer. This emerging class of semiconductors provides strain and composition as degrees of freedom to control the bandgap energy thus covering the entire mid-infrared range. The proposed thermophotovoltaic device is composed of a fully relaxed Ge$_{0.83}$Sn$_{0.17}$ double heterostructure corresponding to a bandgap energy of 0.29 eV. A theoretical framework is derived to evaluate cell performance under high injection. The black-body radiation absorption is investigated using the generalized transfer matrix method thereby considering the mixed coherent/incoherent layer stacking. Moreover, the intrinsic recombination mechanisms and their importance in a narrow bandgap semiconductor were also taken into account. In this regard, the parabolic band approximation and Fermi's golden rule were combined for an accurate estimation of the radiative recombination rate. Based on these analyses, power conversion efficiencies of up to 9% are predicted for Ge$_{0.83}$Sn$_{0.17}$ thermophotovoltaic cells under black-body radiation at temperatures in the 500-1500 K range. A slight improvement in the efficiency is observed under the frontside illumination but vanishes below 800 K, while the use of a backside reflector improves the efficiency across the investigated black-body temperature range. The effects of the heterostructure thickness, surface recombination velocity, and carrier lifetime are also elucidated and discussed.

CMOS-compatible short- and mid-wave infrared emitters are highly coveted for the monolithic integration of silicon-based photonic and electronic integrated circuits to serve a myriad of applications in sensing and communications. In this regard, a group IV germanium-tin (GeSn) material epitaxially grown on silicon (Si) emerges as a promising platform to implement tunable infrared light emitters. Indeed, upon increasing the Sn content, the bandgap of GeSn narrows and becomes direct, making this material system suitable for developing an efficient silicon-compatible emitter. With this perspective, microbridge PIN GeSn LEDs with a small footprint of $1,520$ $\mu$m$^2$ are demonstrated and their operation performance is investigated. The spectral analysis of the electroluminescence emission exhibits a peak at $2.31$ $\mu$m and it red-shifts slightly as the driving current increases. It is found that the microbridge LED operates at a dissipated power as low as $10.8$ W at room temperature and just $3$ W at $80$ K. This demonstrated low operation power is comparable to that reported for LEDs having a significantly larger footprint reaching $10^6$ $\mu$m$^2$. The efficient thermal dissipation of the current design helped to reduce the heat-induced optical losses, thus enhancing light emission. Further performance improvements are envisioned through thermal and optical simulations of the microbridge design. The use of GeSnOI substrate for developing a similar device is expected to improve optical confinement for the realization of electrically driven GeSn lasers.

Transverse vibrations can induce the non-linear compression of a thin film of air to levitate objects, via the squeeze film effect. This phenomenon is well captured by the Reynolds' lubrication theory, however, the same theory fails to describe this levitation when the fluid is incompressible. In this case, the computation predicts no steady-state levitation, contradicting the documented experimental evidence. In this letter, we uncover the main source of the time-averaged pressure asymmetry in the incompressible fluid thin film, leading the levitation phenomenon to exist. Furthermore, we reveal the physical law governing the steady-state levitation height, which we confirm experimentally.

The monolithic integration of extended short-wave infrared (e-SWIR) photodetectors (PDs) on silicon is highly sought-after to implement manufacturable, cost-effective sensing and imaging technologies. With this perspective, GeSn PIN PDs have been the subject of extensive investigations because of their bandgap tunability and silicon compatibility. However, due to growth defects, these PDs suffer a relatively high dark current density as compared to commercial III-V PDs. Herein, we elucidate the mechanisms governing the dark current in $2.6 \, \mu$m GeSn PDs at a Sn content of $10$ at.%. It was found that in the temperature range of $293 \, $K -- $363 \,$K and at low bias, the diffusion and Shockley-Read-Hall (SRH) leakage mechanisms dominate the dark current in small diameter ($20 \, \mu$m) devices, while combined SRH and trap assisted tunneling (TAT) leakage mechanisms are prominent in larger diameter ($160 \, \mu$m) devices. However, at high reverse bias, TAT leakage mechanism becomes dominant regardless of the operating temperature and device size. The effective non-radiative carrier lifetime in these devices was found to reach $\sim 300$ -- $400$ ps at low bias. Owing to TAT leakage current, however, this lifetime reduces progressively as the bias increases.

The prospect of GeSn semiconductors for silicon-integrated infrared optoelectronics brings new challenges related to the metastability of this class of materials. As a matter of fact, maintaining a reduced thermal budget throughout all processing steps of GeSn devices is essential to avoid possible material degradation. This constraint is exacerbated by the need for higher Sn contents along with an enhanced strain relaxation to achieve efficient mid-infrared devices. Herein, as a low thermal budget solution for post-epitaxy processing, we elucidate the effects of laser thermal annealing (LTA) on strain-relaxed Ge$_{0.89}$Sn0$_{.11}$ layers and Ni-Ge$_{0.89}$Sn0$_{.11}$ contacts. Key diffusion and recrystallization processes are proposed and discussed in the light of systematic microstructural studies. LTA treatment at a fluence of 0.40 J/cm2 results in a 200-300 nm-thick layer where Sn atoms segregate toward the surface and in the formation of Sn-rich columnar structures in the LTA-affected region. These structures are reminiscent to those observed in the dislocation-assisted pipe-diffusion mechanism, while the buried GeSn layers remain intact. Moreover, by tailoring the LTA fluence, the contact resistance can be reduced without triggering phase separation across the whole GeSn multi-layer stacking. Indeed, a one order of magnitude decrease in the Ni-based specific contact resistance was obtained at the highest LTA fluence, thus confirming the potential of this method for the functionalization of direct bandgap GeSn materials.

Group IV Ge1-xSnx semiconductors hold the premise of enabling broadband silicon-integrated infrared optoelectronics due to their tunable bandgap energy and directness. Herein, we exploit these attributes along with the enhanced lattice strain relaxation in Ge/Ge0.92Sn0.08 core-shell nanowire heterostructures to implement highly responsive, room-temperature short-wave infrared nanoscale photodetectors. Atomic-level studies confirm the uniform shell composition and its higher crystallinity with respect to thin films counterparts. The demonstrated Ge/Ge0.92Sn0.08 p-type field-effect nanowire transistors exhibit superior optoelectronic properties achieving simultaneously a relatively high mobility, a high ON/OFF ratio, and a high responsivity, in addition to a broadband absorption in the short-wave infrared range. Indeed, the reduced bandgap of the Ge0.92Sn0.08 shell yields an extended cutoff wavelength of 2.1 um, with a room-temperature responsivity reaching 2.7 A/W at 1550 nm. These results highlight the potential of Ge/Ge1-xSnx core/shell nanowires as silicon-compatible building blocks for nanoscale integrated infrared photonics., Comment: 22 pages, 4 figures, 1 Tables, 5 Supplementary information Figures

The availability of high-frequency pulsed emitters in the $2-2.5\,\mu$m wavelength range paved the way for a wealth of new applications in ultrafast spectroscopy, free-space and fiber-optical communications, surveillance and recognition, artificial intelligence, and medical imaging. However, developing these emerging technologies and their large-scale use depend on the availability of high-speed, low-noise, and cost-effective photodetectors. With this perspective, here we demonstrate GeSn photodiodes grown on silicon wafers featuring a high broadband operation covering the extended-SWIR range with a peak responsivity of 0.3 A/W at room temperature. These GeSn devices exhibit a high bandwidth reaching 7.5 GHz at 5 V bias with a 2.6 $\mu$m cutoff wavelength, and their integration in ultrafast time-resolved spectroscopy applications is demonstrated. In addition to enabling time-resolved electro-luminescence at 2.3 $\mu$m, the high-speed operation of GeSn detectors was also exploited in the diagnostics of ultra-short pulses of a supercontinuum laser with a temporal resolution in the picosecond range at 2.5 $\mu$m. Establishing these capabilities highlights the potential of manufacturable GeSn photodiodes for silicon-integrated high-speed extended-SWIR applications.