arXiv subjects

David Jimenez

Publications and source records attributed to David Jimenez.

17 recordsLinked to original sources

arXiv2026

Towards Scalable Federated Container Orchestration: The CODECO Approach

This paper presents CODECO, a federated orchestration framework for Kubernetes that addresses the limitations of cloud-centric deployment. CODECO adopts a data-compute-network co-orchestration approach to support heterogeneous infrastructures, mobility, and multi-provider operation. CODECO extends Kubernetes with semantic application models, partition-based federation, and AI-assisted decision support, enabling context-aware placement and adaptive management of applications and their micro-services across federated environments. A hybrid governance model combines centralized policy enforcement with decentralized execution and learning to preserve global coherence while supporting far Edge autonomy. The paper describes the architecture and core components of CODECO, outlines representative orchestration workflows, and introduces a software-based experimentation framework for reproducible evaluation in federated Edge-Cloud infrastructure environments.

cs.DC↗

arXiv2025

An extended low-frequency noise compact model for single-layer graphene FETs including correlated mobility fluctuations effect

Correlated mobility fluctuations are considered in the physics-based carrier number fluctuation deltaN low-frequency noise (LFN) compact model of single-layer graphene field effect transistors (GFET) in the present study. Trapped charge density and Coulomb scattering coefficient deltaN LFN parameters are obtained after applying a parameter extraction methodology, adapted from conventional silicon technologies, to the linear ambipolar regions of GFETs. Appropriate adjustments are considered in the method according to GFETs physical characteristics. Afterwards, Hooge mobility as well as series resistance fluctuations LFN parameters can be extracted. The updated LFN model is validated with experimental data from various long and short-channel GFETs at an extended range of gate and drain bias conditions.

cond-mat.mes-hall↗

arXiv2025

Fine-grained auxiliary learning for real-world product recommendation

Product recommendation is the task of recovering the closest items to a given query within a large product corpora. Generally, one can determine if top-ranked products are related to the query by applying a similarity threshold; exceeding it deems the product relevant, otherwise manual revision is required. Despite being a well-known problem, the integration of these models in real-world systems is often overlooked. In particular, production systems have strong coverage requirements, i.e., a high proportion of recommendations must be automated. In this paper we propose ALC , an Auxiliary Learning strategy that boosts Coverage through learning fine-grained embeddings. Concretely, we introduce two training objectives that leverage the hardest negatives in the batch to build discriminative training signals between positives and negatives. We validate ALC using three extreme multi-label classification approaches in two product recommendation datasets; LF-AmazonTitles-131K and Tech and Durables (proprietary), demonstrating state-of-the-art coverage rates when combined with a recent threshold-consistent margin loss.

cs.CL↗

arXiv2025

Variability-finding in Rubin Data Preview 1 with LSDB

The Vera C. Rubin Observatory recently released Data Preview 1 (DP1) in advance of the upcoming Legacy Survey of Space and Time (LSST), which will enable boundless discoveries in time-domain astronomy over the next ten years. DP1 provides an ideal sandbox for validating innovative data analysis approaches for the LSST mission, whose scale challenges established software infrastructure paradigms. This note presents a pair of such pipelines for variability-finding using powerful software infrastructure suited to LSST data, namely the HATS (Hierarchical Adaptive Tiling Scheme) format and the LSDB framework, developed by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) Frameworks team. This article presents a pair of variability-finding pipelines built on LSDB, the HATS catalog of DP1 data, and preliminary results of detected variable objects, two of which are novel discoveries.

astro-ph.IM↗

arXiv2023

Gate Tunable Lateral 2D pn Junctions: An Analytical Study of Its Electrostatics

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.

cond-mat.mes-hall↗

arXiv2021

A small-signal description of black phosphorus transistor technologies for high-frequency applications

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.

physics.app-ph↗

arXiv2021

Multifunctional high-frequency circuit capabilities of ambipolar carbon nanotube FETs

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.

physics.app-ph↗

arXiv2021

Input referred low-frequency noise analysis for single-layer graphene FETs

The bias-dependence of input referred low-frequency noise (LFN), SVG, is a considerable facet for RF circuit design. SVG was considered constant in silicon transistors but this was contradicted by recent experimental and theoretical studies. In this letter, the behaviour of SVG is investigated for single-layer graphene transistors based on a recently established physics-based complete compact LFN model. A minimum of SVG is recorded at the bias point where maximum transconductance is located which coincides with the peak of the well-known M-shape of the normalized output LFN and the model precisely captures this trend. Mobility fluctuation effect increases SVG towards to lower currents near charge neutrality point (CNP) while carrier number fluctuation and series resistance effects mostly contribute away from CNP; thus, SVG obtains a parabolic shape vs. gate voltage similarly to CMOS devices.

physics.app-ph↗

arXiv2021

Bias-dependent intrinsic RF thermal noise modeling and characterization of single layer graphene FETs

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.

cond-mat.mes-hall↗

arXiv2020

Bias Dependent Variability of Low Frequency Noise in Single Layer Graphene FETs

Low-frequency noise (LFN) variability in graphene transistors (GFETs) is for the first time researched in this work. LFN from an adequate statistical sample of long-channel solution-gated single-layer GFETs is measured in a wide range of operating conditions while a physics-based analytical model is derived that accounts for the bias dependence of LFN variance with remarkable performance. It is theoretically proved and experimentally validated that LFN deviations in GFETs stem from physical mechanisms that generate LFN. Thus, carrier number DN due to trapping/detrapping process and mobility fluctuations Dm which are the main causes of LFN, define its variability likewise as its mean value. DN accounts for an M-shape of normalized LFN variance versus gate bias with a minimum at the charge neutrality point (CNP) as it was the case for normalized LFN mean value while Dm contributes only near the CNP for both variance and mean value. Trap statistical nature is experimentally shown to differ from classical Poisson distribution at silicon-oxide devices, and this is probably caused by electrolyte interface in GFETs under study. Overall, GFET technology development is still in a premature stage which might cause pivotal inconsistencies affecting the scaling laws in GFETs of the same process.

physics.app-ph↗

arXiv2020

Low-frequency noise parameter extraction method for single layer graphene FETs

In this paper, a detailed parameter extraction methodology is proposed for low-frequency noise (LFN) in single layer (SL) graphene transistors (GFETs) based on a recently established compact LFN model. Drain current and LFN of two short channel back-gated GFETs (L=300, 100 nm) were measured at lower and higher drain voltages, for a wide range of gate voltages covering the region away from charge neutrality point (CNP) up to CNP at p-type operation region. Current-voltage (IV) and LFN data were also available from a long channel SL top solution-gated (SG) GFET (L=5 um), for both p- and n-type regions near and away CNP. At each of these regimes, the appropriate IV and LFN parameters can be accurately extracted. Regarding LFN, mobility fluctuation effect is dominant at CNP and from there the Hooge parameter aH can be extracted while the carrier number fluctuation contribution which is responsible for the well-known M-shape bias dependence of output noise divided by squared drain current, also observed in our data, makes possible the extraction of the NT parameter related to the number of traps. In the less possible case of a Lambda-shape trend, NT and aH can be extracted simultaneously from the region near CNP. Away from CNP, contact resistance can have a significant contribution to LFN and from there the relevant parameter SDR^2 is defined. The LFN parameters described above can be estimated from the low drain voltage region of operation where the effect of Velocity Saturation (VS) mechanism is negligible. VS effect results in the reduction of LFN at higher drain voltages and from there the IV parameter hOmega which represents the phonon energy and is related to VS effect can be derived both from drain current and LFN data.

physics.app-ph↗

arXiv2019

Electrostatics of metal-graphene interfaces: sharp p-n junctions for electron-optical applications

Creation of sharp lateral p-n junctions in graphene devices, with transition widths well below the Fermi wavelength of graphene charge carriers, is vital to study and exploit these electronic systems for electron-optical applications. The achievement of such junctions is, however, not trivial due to the presence of a considerable out-of-plane electric field in lateral p-n junctions, resulting in large widths. Metal-graphene interfaces represent a novel, promising and easy to implement technique to engineer such sharp lateral p-n junctions in graphene field-effect devices, in clear contrast to the much wider (i.e. smooth) junctions achieved via conventional local gating. In this work, we present a systematic and robust investigation of the electrostatic problem of metal-induced lateral p-n junctions in gated graphene devices for electron-optics applications, systems where the width of the created junctions is not only determined by the metal used but also depends on external factors such as device geometries, dielectric environment and different operational parameters such as carrier density and temperature. Our calculations demonstrate that sharp junctions can be achieved via metal-graphene interfaces at room temperature in devices surrounded by dielectric media with low relative permittivity. In addition, we show how specific details such as the separation distance between metal and graphene and the permittivity of the gap in-between plays a critical role when defining the p-n junction, not only defining its width w but also the energy shift of graphene underneath the metal. These results can be extended to any two-dimensional (2D) electronic system doped by the presence of metal clusters and thus are relevant for understanding interfaces between metals and other 2D materials.

cond-mat.mes-hall↗

arXiv2012

Drift-diffusion model for single layer transition metal dichalcogenide field-effect transistors

A physics-based model for the surface potential and drain current for monolayer transition metal dichalcogenide (TMD) field-effect transistor (FET) is presented. Taking into account the 2D density-of-states of the atomic layer thick TMD and its impact on the quantum capacitance, a model for the surface potential is presented. Next, considering a drift-diffusion mechanism for the carrier transport along the monolayer TMD, an explicit expression for the drain current has been derived. The model has been benchmarked with a measured prototype transistor. Based on the proposed model, the device design window targeting low-power applications is discussed.

cond-mat.mes-hall↗

arXiv2011

Explicit drain-current model of graphene field-effect transistors targeting analog and radio-frequency applications

We present a compact physics-based model of the current-voltage characteristics of graphene field-effect transistors, of especial interest for analog and radio-frequency applications where bandgap engineering of graphene could be not needed. The physical framework is a field-effect model and drift-diffusion carrier transport. Explicit closed-form expressions have been derived for the drain current covering continuosly all operation regions. The model has been benchmarked with measured prototype devices, demonstrating accuracy and predictive behavior. Finally, we show an example of projection of the intrinsic gain as a figure of merit commonly used in RF /analog applications.

cond-mat.mes-hall↗

arXiv2010

Analytic model for the surface potential and drain current in negative capacitance field-effect transistors

In 2008, Salahuddin and Datta proposed that a ferroelectric material operating in the negative capacitance region could act as a step-up converter of the surface potential in a MOS structure, opening a new route for the realization of transistors with steeper subthreshold characteristics (S<60 mV/decade). In this letter, a comprehensive physics-based surface potential and drain current model for the negative capacitance field-effect transistor is reported. The model is aimed to evaluate the potentiality of such transistors for low-power switching applications. Moreover it provides a model core for memories devices relying on the hysteretic behavior of the ferroelectric gate insulator.

cond-mat.mes-hall↗

arXiv2008

A current-voltage model for Schottky-barrier graphene based transistors

A low complexity computational model of the current-voltage characteristics for graphene nano-ribbon (GNR) field effect transistors (FET), able to simulate a hundred of points in few seconds using a PC, is presented. For quantum capacitance controlled devices, self-consistent calculations of the electrostatic potential can be skipped. Instead, analytical closed-form electrostatic potential from Laplace's equation yields accurate results compared with that obtained by self-consistent Non-Equilibrium Green's Functions (NEGF) method. The model includes both tunnelling current through the Schottky barrier (SB) at the contact interfaces and thermionic current above the barrier, properly capturing the effect of arbitrary physical and electrical parameters.

cond-mat.mes-hall↗

arXiv2007

Magnetically Induced Field Effect in Carbon Nanotube Devices

Three-terminal devices with conduction channels formed by quasi-metallic carbon nanotubes (CNT) are shown to operate as nanotube-based field-effect transistors under strong magnetic fields. The off-state conductance of the devices varies exponentially with the magnetic flux intensity. We extract the quasi-metallic CNT chirality as well as the characteristics of the Schottky barriers formed at the metal-nanotube contacts from temperature-dependent magnetoconductance measurements.

cond-mat.mes-hall↗