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Extreme Multi-label Classification (XMC) methods predict relevant labels for a given query in an extremely large label space. Recent works in XMC address this problem using deep encoders that project text descriptions to an embedding space suitable for recovering the closest labels. However, learning deep models can be computationally expensive in large output spaces, resulting in a trade-off between high performing brute-force approaches and efficient solutions. In this paper, we propose PRIME, a XMC method that employs a novel prototypical contrastive learning technique to reconcile efficiency and performance surpassing brute-force approaches. We frame XMC as a data-to-prototype prediction task where label prototypes aggregate information from related queries. More precisely, we use a shallow transformer encoder that we coin as Label Prototype Network, which enriches label representations by aggregating text-based embeddings, label centroids and learnable free vectors. We jointly train a deep encoder and the Label Prototype Network using an adaptive triplet loss objective that better adapts to the high granularity and ambiguity of extreme label spaces. PRIME achieves state-of-the-art results in several public benchmarks of different sizes and domains, while keeping the model efficient.

In this correspondence, we analyze the performance of a reconfigurable intelligent surface (RIS)-aided communication system that involves a fluid antenna system (FAS)-enabled receiver. By applying the central limit theorem (CLT), we derive approximate expressions for the system outage probability when the RIS has a large number of elements. Also, we adopt the block-correlation channel model to simplify the outage probability expressions, reducing the computational complexity and shedding light on the impact of the number of ports. Numerical results validate the effectiveness of our analysis, especially in scenarios with a large number of RIS elements.

The advent of the sixth-generation (6G) networks presents another round of revolution for the mobile communication landscape, promising an immersive experience, robust reliability, minimal latency, extreme connectivity, ubiquitous coverage, and capabilities beyond communication, including intelligence and sensing. To achieve these ambitious goals, it is apparent that 6G networks need to incorporate the state-of-the-art technologies. One of the technologies that has garnered rising interest is fluid antenna system (FAS) which represents any software-controllable fluidic, conductive, or dielectric structure capable of dynamically changing its shape and position to reconfigure essential radio-frequency (RF) characteristics. Compared to traditional antenna systems (TASs) with fixed-position radiating elements, the core idea of FAS revolves around the unique flexibility of reconfiguring the radiating elements within a given space. One recent driver of FAS is the recognition of its position-flexibility as a new degree of freedom (dof) to harness diversity and multiplexing gains. In this paper, we provide a comprehensive tutorial, covering channel modeling, signal processing and estimation methods, information-theoretic insights, new multiple access techniques, and hardware designs. Moreover, we delineate the challenges of FAS and explore the potential of using FAS to improve the performance of other contemporary technologies. By providing insights and guidance, this tutorial paper serves to inspire researchers to explore new horizons and fully unleash the potential of FAS., Comment: 53 pages, 45 figures, 5 tables. Accepted by IEEE Communications Surveys and Tutorials

The conservation and authentication of pictorial artworks is considered an important part of the preservation of the cultural heritage. The use of non-destructive testing allows the obtaining of accurate information about the state of pictorial artworks, without direct contact between the equipment used and the sample. In particular, the use of this kind of technology is recommended in obtaining three-dimensional surface digital models, as it provides high-resolution information that constitutes a kind of fingerprint of the samples. In the case of pictorial artworks with some kind of surface relief, one of the most useful technologies is structured light (SL). In this paper the minimum difference in height that can be distinguished with this technology is estimated, establishing experimentally both the error committed in the measurement process and the precision in the use of this technology. The study, focused on the case of oil paintings on canvas, has been developed using a low-cost system to ensure its wide use.

The main target of this article is to propose for the first time a physics-based continuous and symmetric compact model that accurately captures IV experimental dependencies induced by geometrical scaling effects for graphene transistor (GFET) technologies. Such a scalable model is an indispensable ingredient for the boost of large-scale GFET applications, as it has been already proved in solid industry-based CMOS technologies. Dependencies of the physical model parameters on channel dimensions, are thoroughly investigated, and semi?empirical expressions are derived, which precisely characterize such behaviors for an industry-based GFET technology, as well as for others developed in research labs. This work aims at the establishment of the first industry standard GFET compact model that can be integrated in circuit simulation tools and hence, can contribute to the update of GFET technology from the research level to massive industry production.

Powered by position-flexible antennas, the emerging fluid antenna system (FAS) technology is postulated as a key enabler for massive connectivity in 6G networks. The free movement of antenna elements enables the opportunistic minimization of interference, allowing several users to share the same radio channel without the need of precoding. However, the true potential of FAS is still unknown due to the extremely high spatial correlation of the wireless channel between very close-by antenna positions. To unveil the multiplexing capabilities of FAS, proper (simple yet accurate) modeling of the spatial correlation is prominently needed. Realistic classical models such as Jakes's are prohibitively complex, rendering intractable analyses, while state-of-the-art approximations often are too simplistic and poorly accurate. Aiming to fill this gap, we here propose a general framework to approximate spatial correlation by block-diagonal matrices, motivated by the well-known block fading assumption and by statistical results on large correlation matrices. The proposed block-correlation model makes the performance analysis possible, and tightly approximates the results obtained with realistic models (Jakes's and Clarke's). Our framework is leveraged to analyze fluid antenna multiple access (FAMA) systems, evaluating their performance for both one- and two-dimensional fluid antennas.

Graphene field-effect transistors exhibit negligible transconductance under two scenarios: for any gate-to-source voltage when the drain-to-source voltage is set to zero, and for an arbitrary drain-to-source voltage provided that the gate-to-source voltage equals the Dirac voltage. Hence, extracting the channel and the parasitic series resistances from S-parameters under these conditions enables analyzing their dependence on the gate and drain biases. This is fundamental to assess the portion of the output resistance that is controlled by the gate. Besides, the drain bias dependence of the drain and source resistances is also evidenced. Within the proposal, resistive components accounting for the lossy nature of the gate capacitance are incorporated into the model, which exhibits a broadband correlation with experimental data. This avoids the series resistances to be considered as frequency dependent in the model.

Exploiting ambipolar electrical conductivity based on graphene field-effect transistors has raised enormous interest for high-frequency (HF) analog electronics. Controlling the device polarity, by biasing the graphene transistor around the vertex of the V-shaped transfer curve, enables to redesign and highly simplify conventional analog circuits, and simultaneously to seek for multifunctionalities specially in the HF domain. We present, here, new insights for the design of different HF applications such as power amplifiers, mixers, frequency multipliers, phase shifters, and modulators that specifically leverage the inherent ambipolarity of graphene-based transistors., Comment: 31 pages, 7 figures

Textual noise, such as typos or abbreviations, is a well-known issue that penalizes vanilla Transformers for most downstream tasks. We show that this is also the case for sentence similarity, a fundamental task in multiple domains, e.g. matching, retrieval or paraphrasing. Sentence similarity can be approached using cross-encoders, where the two sentences are concatenated in the input allowing the model to exploit the inter-relations between them. Previous works addressing the noise issue mainly rely on data augmentation strategies, showing improved robustness when dealing with corrupted samples that are similar to the ones used for training. However, all these methods still suffer from the token distribution shift induced by typos. In this work, we propose to tackle textual noise by equipping cross-encoders with a novel LExical-aware Attention module (LEA) that incorporates lexical similarities between words in both sentences. By using raw text similarities, our approach avoids the tokenization shift problem obtaining improved robustness. We demonstrate that the attention bias introduced by LEA helps cross-encoders to tackle complex scenarios with textual noise, specially in domains with short-text descriptions and limited context. Experiments using three popular Transformer encoders in five e-commerce datasets for product matching show that LEA consistently boosts performance under the presence of noise, while remaining competitive on the original (clean) splits. We also evaluate our approach in two datasets for textual entailment and paraphrasing showing that LEA is robust to typos in domains with longer sentences and more natural context. Additionally, we thoroughly analyze several design choices in our approach, providing insights about the impact of the decisions made and fostering future research in cross-encoders dealing with typos., Comment: KDD'23 conference (main research track). (*) These authors contributed equally

A first modeling approximation of the general performance of radiofrequency (RF) switches based on hexagonal boron nitride (hBN), a two-dimensional (2D) dielectric material is presented. The I-V characteristics intrinsic and extrinsic impedance parameters, the return loss, insertion loss and isolation of RF 2D switches fabricated with hBN are described here by a equivalent circuit models. Straightforward analytical expressions are obtained. In contrast to conventional switches, the unique RF performance of the hBN switch, at ON-state, i.e., a direct improvement with frequency of the insertion loss, is accurately described by considering a capacitor in the intrinsic part of the model. The latter is suggested to be related to storaged charge during the resistive switching mechanism. The highest mean relative error obtained between modeling and measurements of the return loss is of 7.6% with the approach presented here which overcomes the 42.5% of difference obtained with a previous model with an incomplete intrinsic device description.

1/f noise is a critical figure of merit for the performance of transistors and circuits. For two-dimensional devices (2D-FETs), and especially for applications in the GHz range where short-channel FETs are required, velocity saturation (VS) effect can result in the reduction of 1/f noise at high longitudinal electric fields. A new physics-based compact model is for the first time introduced for single- to few- layer 2D-FETs in this study, precisely validating 1/f noise experiments for various types of devices. The proposed model mainly accounts for the measured 1/f noise bias dependence as the latter is defined by different physical mechanisms. Thus, analytical expressions are derived, valid in all regions of operation in contrast to conventional approaches available in literature so far, accounting for carrier number fluctuation (DN), mobility fluctuation (Dmu}) and contact resistance (DR) effects based on the underlying physics that rules these devices. DN mechanism due to trapping/detrapping together with an intense Coulomb scattering effect, dominates 1/f noise from medium to strong accumulation region while Dmu, is also demonstrated to modestly contribute in subthreshold region. DR can also be significant in very high carrier density. The VS induced reduction of 1/f noise measurements at high electric fields, is also remarkably captured by the model. The physical validity of the model can also assist in extracting credible conclusions when conducting comparisons between experimental data from devices with different materials or dielectrics., Comment: Nanoscale, 2023, Advance Article

We propose an explicit small-signal graphene field-effect transistor (GFET) parameter extraction procedure based on a charge-based quasi-static model. The dependence of the small-signal parameters on both gate voltage and frequency is precisely validated by high-frequency (up to 18 GHz) on-wafer measurements from a 300 nm device. These parameters are studied simultaneously, in contrast to other works which focus exclusively on few. Efficient procedures have been applied to GFETs for the first time to remove contact and gate resistances from the Y-parameters. The use of these methods yields straightforward equations for extracting the small-signal model parameters, which is extremely useful for radio-frequency circuit design. Furthermore, we show for the first time experimental validation vs. both gate voltage and frequency of the intrinsic GFET non-reciprocal capacitance model. Accurate models are also presented for the gate voltage-dependence of the measured unity-gain and maximum oscillation frequencies as well as of the current and power gains.

The electrostatics of two-dimensional (2D) lateral pn homojunctions considering the impact of electrostatic doping by means of two split bottom-gates are studied here. Analytical expressions are obtained from the solution of the 2D Poisson equation considering a depletion approximation. Straightforward analytical models for the electrostatic potential and the depletion width within both the dielectric and the 2D semiconductor are obtained for both the symmetrical and asymmetrical cases. In contrast to the case of devices with chemical doping, the obtained depletion width model of devices with electrostatic doping do not depend on the dielectric constant but only on the electrostatic potential and oxide thickness. The models describe the electrostatics of gate-tunable 2D pn junctions at arbitrary bias. A benchmark against numerical device simulations of MoS2-based pn junctions validate the analytical models.

In this study, we report the progress made towards the definition of a modular compact modeling technology for graphene field-effect transistors (GFET) that enables the electrical analysis of arbitrary GFET-based integrated circuits. A set of primary models embracing the main physical principles defines the ideal GFET response under DC, transient (time domain), AC (frequency domain), and noise (frequency domain) analysis. Other set of secondary models accounts for the GFET non-idealities, such as extrinsic-, short-channel-, trapping/detrapping-, self-heating-, and non-quasi static-effects, which could have a significant impact under static and/or dynamic operation. At both device and circuit levels, significant consistency is demonstrated between the simulation output and experimental data for relevant operating conditions. Additionally, we provide a perspective of the challenges during the scale up of the GFET modeling technology towards higher technology readiness levels while drawing a collaborative scenario among fabrication technology groups, modeling groups, and circuit designers., Comment: 61 pages, 18 figures, Supplementary Information 3 pages

The potential barrier height at the interface formed by a metal contact and multiple one-dimensional (1D) quasi-ballistic channels in field-effect transistors (FETs) is evaluated across different carbon nanotube and nanowire device technologies by means of a Landauer-B\"uttiker-based extraction methodology (LBM) adapted for multiple 1D-channels. The extraction methodology yields values for an effective Schottky barrier height and a gate coupling coefficient, an indicator of the device working at the quantum capacitance limit. The novel LBM-based approach embracing the mechanisms in 1D electronics is compared to the conventional activation energy method not considering such effects. The latter approach underestimates the potential barrier height at metal-channel interfaces in comparison to the novel methodology. A test structure based on a displaced gate device is proposed based on numerical device simulation results towards an improved accuracy of the method.

Product matching is a fundamental step for the global understanding of consumer behavior in e-commerce. In practice, product matching refers to the task of deciding if two product offers from different data sources (e.g. retailers) represent the same product. Standard pipelines use a previous stage called blocking, where for a given product offer a set of potential matching candidates are retrieved based on similar characteristics (e.g. same brand, category, flavor, etc.). From these similar product candidates, those that are not a match can be considered hard negatives. We present Block-SCL, a strategy that uses the blocking output to make the most of Supervised Contrastive Learning (SCL). Concretely, Block-SCL builds enriched batches using the hard-negatives samples obtained in the blocking stage. These batches provide a strong training signal leading the model to learn more meaningful sentence embeddings for product matching. Experimental results in several public datasets demonstrate that Block-SCL achieves state-of-the-art results despite only using short product titles as input, no data augmentation, and a lighter transformer backbone than competing methods., Comment: 7 pages, 2 figures, e-commerce, conference

The intrinsic mobility degradation coefficient, contact resistance and the transconductance parameter of graphene field-effect transistors (GFETs) are extracted for different technologies by considering a novel transport model embracing mobility degradation effects within the charge channel control description. By considering the mobility degradation-based model, a straightforward extraction methodology, not provided before, is enabled by applying the concept of the well-known Y-function to the \textit{I-V} device characteristics. The method works regardless the gate device architecture. An accurate description of experimental data of fabricated devices is achieved with the underlying transport equation by using the extracted parameters. An evaluation of the channel resistance, enabled by the extracted parameters here, has been also provided.

Directional beamforming will play a paramount role in 5G and beyond networks in order to combat the higher path losses incurred at millimeter wave bands. Appropriate modeling and analysis of the angles and distances between transmitters and receivers in these networks are thus essential to understand performance and limiting factors. Most existing literature considers either infinite and uniform networks, where nodes are drawn according to a Poisson point process, or finite networks with the reference receiver placed at the origin of a disk. Under either of these assumptions, the distance and azimuth angle between transmitter and receiver are independent, and the angle follows a uniform distribution between $0$ and $2\pi$. Here, we consider a more realistic case of finite networks where the reference node is placed at any arbitrary location. We obtain the joint distribution between the distance and azimuth angle and demonstrate that these random variables do exhibit certain correlation, which depends on the shape of the region and the location of the reference node. To conduct the analysis, we present a general mathematical framework which is specialized to exemplify the case of a rectangular region. We then also derive the statistics for the 3D case where, considering antenna heights, the joint distribution of distance, azimuth and zenith angles is obtained. Finally, we describe some immediate applications of the present work, including the analysis of directional beamforming, the design of analog codebooks and wireless routing algorithms., Comment: 16 pages, 10 figures

We have investigated the electrostatics and electronic transport of the gate tunable 2D pn junction by implementing a comprehensive physics-based simulator that self-consistently solves the 2D Poisson's equation coupled to the drift-diffusion current and continuity equations. The simulator considers the strong influence of the out-of-plane electric field through the gate dielectric and the presence of interface states. The impact of parameters such as gate capacitance, energy gap and interface trap states density have been considered to model properties such as the depletion width, rectification factor and depletion and diffusion capacitances. The present work opens the door to a wider exploration of potential advantages that gate tunable 2D pn junctions could bring in terms of figures of merit., Comment: 12 pages, 8 figures

We present a physics-based circuit-compatible model for pH-sensitive field-effect transistors based on two-dimensional (2D) materials. The electrostatics along the electrolyte-gated 2D-semiconductor stack is treated by solving the Poisson equation including the Site-Binding model and the Gouy-Chapman-Stern approach, while the carrier transport is described by the drift-diffusion theory. The proposed model is provided in an analytical form and then implemented in Verilog-A, making it compatible with standard technology computer-aided design tools employed for circuit simulation. The model is benchmarked against two experimental transition-metal-dichalcogenide (MoS2 and ReS2) based ion sensors, showing excellent agreement when predicting the drain current, threshold voltage shift, and current/voltage sensitivity measurements for different pH concentrations., Comment: 4 pages, 3 figures, 1 table

This work presents a small-signal high-frequency (HF) equivalent circuit (EC) to model AC performances of black-phosphorous field-effect transistors (BPFETs). The proposed EC is able to describe correctly both the experimental HF intrinsic and extrinsic figures of merit, as well as S-parameters, from different BPFET technologies. Single- and double-stage radio frequency gain amplifiers, are designed at 2.4 GHz using the experimentally-calibrated small-signal BPFET EC. Results show high-gain high-selective BPFET-based amplifiers.

An experimentally-calibrated carbon nanotube compact transistor model has been used here to design two high-frequency (HF) circuits with two different functionalities each: a phase configurable amplifier (PCA) and a frequency configurable amplifier (FCA). The former design involves an in-phase amplifier and an inverting amplifier while the latter design embraces a frequency doubler as well as a distinct inverting amplifier. The specific functionality selection of each of the two HF circuit designs is enabled mainly by the inherent ambipolar feature at a device level. Furthermore, at a circuit level the matching networks are the same regardless the operation mode. In-phase and inverting amplification are enabled in the PCA by switching the gate-to-source voltage (VGS) from -0.3 V to 0.9 V while the drain-to-source voltage (VDS) remains at 3 V. By designing carefully the matching and stability networks, power gains of 4.5 dB and 6.7 dB at 2.4 GHz for the in-phase and inverting operation mode have been achieved, respectively. The FCA, in its frequency doubler operation mode, exhibits 20 dBc of fundamental-harmonic suppression at 2.4 GHz when an input signal at 1.2 GHz is considered. This frequency doubler functionality is enabled at VGS=0.3 V, whereas at VGS=0.9 V amplification of 4.5 dB is obtained while VDS remains at 3 V in both cases. In both configurable circuits the stabilization and matching networks are the same regardless the bias-chosen operation mode. The circuits performance degradation due to metallic tubes in the device channel is studied as well as the impact of non-ideal inductors in each design. PCA and FCA operation modes are further exploited in high-frequency modulators.

The Dirac voltage of a graphene field-effect transistor (GFET) stands for the gate bias that sets the charge neutrality condition in the channel, thus resulting in a minimum conductivity. Controlling its dependence on the terminal biases is crucial for the design and optimization of radio-frequency applications based on multiple GFETs. However, the previous analysis of such dependence carried out for a single device can lead to confusion and if not properly understood could result in circuit designs with poor performance. The control of the Dirac point shift (DPS) is particularly important for the deployment of graphene-based differential circuit topologies where keeping a strict symmetry between the electrical balanced branches is crucial for exploiting the advantages of such topologies. This note sheds light on the impact of terminal biases on the DPS in a real device and sets a rigorous methodology to control it so to eventually optimize and exploit the performance of radio-frequency applications based on GFETs., Comment: 7 pages, 3 figures, 1 table

In this article, the bias-dependence of intrinsic channel thermal noise of single-layer graphene field-effect transistors (GFETs) is thoroughly investigated by experimental observations and compact modeling. The findings indicate an increase of the specific noise as drain current increases whereas a saturation trend is observed at very high carrier density regime. Besides, short-channel effects like velocity saturation also result in an increment of noise at higher electric fields. The main goal of this work is to propose a physics-based compact model that accounts for and accurately predicts the above experimental observations in short-channel GFETs. In contrast to long-channel MOSFET based models adopted previously to describe thermal noise in graphene devices without considering the degenerate nature of graphene, in this work a model for short-channel GFETs embracing the 2D materials underlying physics and including a bias dependency is presented. The implemented model is validated with de-embedded high frequency data from two short-channel devices at Quasi-Static region of operation. The model precisely describes the experimental data for a wide range of low to high drain current values without the need of any fitting parameter. Moreover, the consideration of the degenerate nature of graphene reveals a significant decrease of noise in comparison with the non degenerate case and the model accurately captures this behavior. This work can also be of outmost significance from circuit designers aspect, since noise excess factor, a very important figure of merit for RF circuits implementation, is defined and characterized for the first time in graphene transistors.