This article presents a physics-informed deep learning (DL) framework for the modeling and optimization of submicron junctionless infrared phototransistors (JLPs). By integrating TCAD-based simulations with DL classification and feature importance analysis, the study identifies key parameters that govern device figures of merit (FoMs) such as I on , I off , I on /I off ratio, and responsivity. Results highlight the critical influence of doping and structural variations on phototransistor performance and demonstrate the utility of DL in guiding low-cost, high-efficiency device design for optical communication systems. Beyond performance prediction, the proposed approach provides valuable design guidelines that capture complex parameter interactions and improve device reliability. This work highlights the potential of physics-informed DL as a strategic tool to accelerate the development of scalable, energy-efficient phototransistors for next-generation low cost CMOS-based optoelectronic applications.

Achieving high efficiency in CZTSSe solar cells requires precise control over both structural and compositional parameters. This work aims to develop a predictive, data-driven design strategy for high-performance and eco-friendly CZTSSe solar cells by optimizing plasmonic and compositional features using deep learning and numerical simulation techniques. We present a comprehensive numerical and deep learning–driven investigation of key design features, including absorber layer thickness, charge transport layer properties, front-contact configuration, embedded gold nanoparticles (Au-NPs), and a tuned S/(S + Se) ratio, which is directly linked to bandgap tuning and electron affinity shifts within the CZTSSe absorber. By combining finite-difference time-domain (FDTD) simulations, the SCAPS-1D tool, and deep learning techniques, we systematically evaluate the role of each parameter in influencing the photovoltaic figures of merit (FoMs). Furthermore, we employ deep learning–enhanced FDTD analysis to explore the synergistic effects of Au-NP size, key design parameters, and S/(S + Se) ratio values on optical absorption and charge extraction performances. The results demonstrate that the integration of Au nanoparticles with optimized bandgap tuning significantly enhances light absorption and charge transport, resulting in an overall power conversion efficiency (PCE) exceeding 23

In this paper, a new ion selective tunneling field effect transistor (IS TFET) based on double gate (DG) aspect and SiSn-Si-Si heterojunction channel is proposed. The device is numerically modeled based on band-to-band tunneling model (BTBT) using ATLAS 2-D simulator. The effect of SiSn alloy as a source region in DG TFET platform on the pH sensor performance is investigated. It is found that the use of 40

Single‐gate metal‐oxide‐semiconductor field‐effect transistors (MOSFET) suffer from several basic physical and electrical limitations for further scaling in deep nanoscale domains, which are often known as short‐channel and reliability effects. The junctionless (JL) gate‐all‐around (GAA) MOSFET design is claimed to provide superior performance and scalability properties as well as reduced elaboration cost in comparison to that of double‐gate and Fin‐FETs. However, further improvements regarding the subthreshold slope (SS) and power consumption should be carried out to better minimize the problem of commutation speed and power dissipation of the nanoscale device. In this context, this work aims at investigating the performance of ferroelectric (FE) JLGAA MOSFET including the impact of the FE material variability on the subthreshold performances, such as subthreshold slope and threshold voltage ( V th ). In this framework, the transistor subthreshold properties will be analytically modeled, where accurate models of subthreshold current, SS and V th will also be derived and validated by technology computer‐aided design (TCAD)‐based numerical simulations. The investigated device is implemented to investigate the performance of a nanoelectronic inverter gate in the subthreshold regime. The proposed investigation can open new paths for developing efficient field effect transistor (FET)‐based low‐power nanoelectronic digital circuits.

With the continuous downscaling of analog CMOS-based circuits, the sensitivity of nanoelectronic devices to design parameter variations has significantly increased. In this paper, we introduce a novel design approach that combines numerical simulations with Machine Learning (ML) analysis to explore the key design parameters of ultra-low scale Junctionless Gate-All-Around (JL GAA) Field-Effect Transistors (FETs). Accurate 3D numerical models that incorporate quantum effects and ballistic transport are employed to simulate the I-V characteristics of 10 nm JL GAA FET devices. The influence of variations in device geometry and doping concentration on analog Figures-of-Merit (FoMs), such as intrinsic gain (Av) and cut-off frequency, is thoroughly analyzed. The use of high-k dielectric materials is also explored for improving the frequency response in high-speed analog circuits. By leveraging ML techniques, the study identifies the optimal design parameters that enhance key analog performance metrics, enabling efficient prediction and optimization of device behavior. Our results highlight the importance of channel radius and channel doping in enhancing the intrinsic gain and frequency response in analog devices. Moreover, the investigated JL GAA FETs exhibit high intrinsic gain and frequency performances, making them ideal candidates for high-speed and high-gain analog circuits. The integration of machine learning techniques further streamlines the optimization process, leading to the identification of optimal parameters that maximize analog performance of the device.

This study investigated a new ultraviolet UV phototransistor structure employing SnO 2 thin-film and plasmonic Ag nanoparticles (NPs). The main objective is to enhance the photogeneration capability in the active layer to achieve enhanced responsibility. Numerical model is developed for the device under examination and the photoresponse characteristics of the device are extracted. Utilizing a 3-D FDTD approach, the optical properties of SnO 2 thin-film coated with Ag NPs are explored, revealing enhanced UV-absorbance capabilities attributed to localized surface plasmon resonance effects (LSPR). The effect of the NPs radius and spacing on the device performance is also analyzed. It is revealed that the proposed device can enhance the device UV photodetection properties offering a high current ratio of 90 dB. This innovative approach using cost-effective SnO 2 active layer decorated with Ag NPs, significantly improves absorption capability of UV laght, offering a promising avenue for the development of alternative phototransistors for optical wireless communication systems (OWCS).

In this work, a new infrared (IR) phototransistor (PT) structure employing thin-film-transistor (TFT) building block and GeSn sensitive layer with Gaussian doping profile (GDP) is proposed. Accurate numerical models based on Silvaco software are developed. The impact of an engineered doping on the performance of the analyzed TFT PT based on GeSn sensitive layer (SL) is investigated. It is found that the the use GD profile can allow achieving enhanced photoresponse characteristics, while maintaining reduced noise effects. This is attributed to enhanced carrier transfer due to the generation of an electric field across the GeSn SL, facilitating carrier injection to the TFT platform. The GD profile is optimized and the associated device performances are compared with recent works, where higher current ratio, improved detectivity and superior responsivity are achieved. This innovative approach using GD profile, significantly enhances the device performances, offering a promising avenue for the development of alternative PTs for optical wireless communication systems (OWCS).

The present study aims at investigating the impact of bimetal Ni-Cu intermediate layer on the performances of thin-film AZO-based structure, as alternative reliable broadband optical sensor. In this regard, this paper provides an in-depth experimental analysis of the Optoelectrical properties of AZO-Ni-Cu-AZO structures deposited on glass substrates for broadband (UV-Vis-NIR) photosensing applications. Employing RF Sputtering for the deposition process, we investigate the integration of Nickel (Ni) to enhance the reliability properties, particularly thermal and electromagnetic radiation effects, within the Aluminum Zinc Oxide (AZO) matrix. Moreover, the prepared structure (AZO-Ni-AZO) is re-engineered by introducing an ultrathin Copper (Cu) layer, in order to enhance the electrical properties and flexibility behavior of the sensor. The investigated AZO-Ni-Cu-AZO structure exhibits improved photodetectivity, which makes it as an alternative for developing advanced optoelectronic devices for reliable, flexible and low-cost thin-film technology.

In this paper, we introduce an innovative design approach centered on optimized source/channel/drain structure using Silicon-Tin (SiSn) binary alloys to enhance the Subthreshold Swing (SS) factor of nanoscale ultra-thin film Double Gate (DG) Tunnel-FET design. In this context, accurate numerical models taking into account tunneling effects and quantum transport are developed. The influence of the Sn mole fraction on the device's switching characteristics and resulting current capability is thoroughly examined. Additionally, the influence of high-k dielectric materials on device performance is analyzed. The optimized SiSn DG TFET device demonstrates a very low Subthreshold Swing factor of 25.5 mV/dec, while maintaining a reduced ambipolar behavior. Therefore, the proposed design framework strategy paves not only to identify the appropriate binary alloys associated with the optimized mole fraction values, but also to develop efficient ultra-low power multigate transistors.

This article provides an in-depth analysis of the optical and electrical characteristics of a multilayer film consisting of AZO-Cu-AZO, which was formed using RF magnetron sputtering. The film has a thickness of 40 nm for both AZO layers and 10 nm for the middle Cu ultra-thin film. The optical characterization shows that the material has a wide range of wavelengths that it can transmit, from 442 nm to 778 nm. This range covers most of the visible and near-infrared spectrum. The material has a peak transmittance of around 70%, which suggests that it could be used as a transparent coating for optoelectronic applications. Hall effect characterization reveals a significant abundance of charge carriers of the n-type, with a concentration of 10 21 cm -3 and a good recorded electron mobility. The results indicate that the AZO-Cu-AZO layer exhibits a high level of electrical conductivity while also preserving a good optical transparency. Moreover, the economic benefits and mechanical flexibility of this structure indicate its potential use in flexible portable electronic devices and other applications. The results emphasize the significance of improving deposition methods in order to get the highest possible material performance for optoelectronic applications.

In this work, we propose a novel design framework based on combined finite-difference time-domain (FDTD) simulations and machine learning (ML) analysis, aiming to improve the light trapping and carrier transport management in the SnS-based thin-film solar cells (TFSCs). The present study aims to identify the key governing solar cell parameters throughout the structure designing processes in order to apply them to develop efficient eco-friendly SnS TFSCs. In this regard, a new SnS solar cell structure based on alternative electron transport layers (ETLs) combined with plasmonic-based light management approach is proposed. Extensive ML-FDTD analysis is performed to assess the influence of the gold nanoparticles (Au-NPs) distribution and carrier transport layers on the TFSC performance. It is revealed that the proposed design framework can predict the best radius and spatial distribution of Au-NPs and the appropriate ETL. This is attributed to the use of ML approach, allowing the selection of the Au-NPs configuration for enhanced light trapping and carrier transport management. Moreover, the numerical simulated current and voltage values show a consistency with the ML-FDTD predictions. Through ML-FDTD analysis, SnS 2 material is found to be the best ETL, while radius and spatial distribution of Au-NPs are other key governing parameters to attain over 12% efficiency. The optimized device shows enhanced open circuit voltage of 0.63 V and improved short circuit current of 27 mA/cm 2 . The obtained results can open new paths in predicting new efficient TFSC structures for eco-friendly photovoltaic applications.

The present study aims at investigating the impact of the ferroelectric materials and channel lengths on the subthreshold swing factor of nanoscale Negative Capacitance Junctionless Gate All Around NCJGAA- MOSFETs. In this regard, analytical modeling of surface potential and subthreshold swing, including short channel and ferroelectric material effects, is performed. The surface potential subthreshold swing models are analytically developed by solving the Poisson’s equation in cylindrical coordinates in the channel region, including the negative capacitance and ferroelectric material effects on the device performance. The subthreshold swing (SS) variation is presented as function of different ferroelectric materials and variable channel length (L), in order to study the scaling capability of the proposed device. The obtained analytical results are in good agreement with numerical simulations. A minimum SS of $52 \mathrm{mV} /$ decade is recorded for $\mathrm{L}=30 \mathrm{~nm}$ using the doped-hafnium oxide as ferroelectric material, making it ideal for usage in low-power and digital nanoelectronic applications.

Perovskite-based tandem solar cells emerged as potential candidates for efficient photovoltaic applications. These devices exhibit high optical absorption properties and tunable direct band-gap. In this work, a novel lead-free Perovskite-SnS Tandem solar cell based on alternative charge transport layers combined with plasmonic-based light management approach is proposed. Accurate numerical investigation is carried out to assess the influence of the charge transport layers of top sub-cell on the optoelectronic properties of the tandem cell. The obtained results reveal the potential of SnO2 and CuO materials as electron and hole transport layers, respectively, demonstrating a good conduction band offset (CBO) and thereby enhanced recombination losses. Furthermore, the role of Gold-nanoparticles in enhancing absorption and light-trapping mechanisms in the bottom SnS-based sub-cell is investigated using FDTD computations. It is found that the optimized tandem cell with Au-NPs exhibits a high power conversion efficiency of 20.1%. Therefore, this work can open up new paths to boost the power conversion of Sn-based Perovskite/SnS Tandem cells for high-performance and eco-friendly photovoltaic applications.

In this work, optical and electrical properties of tin-oxide (SnOx) thin-films were investigated for broadband photosensing and photovoltaic applications. The GLancing Angle Deposition (GLAD) and Reactive Gas Pulsing Process (RGPP) techniques were used to study the effects of the oxygen ratio variation and deposition angle on the film optoelectronic and photovoltaic properties. The deposition angle of the particle flux was kept at 80 degrees and the injected oxygen gas concentration was tuned by changing the oxygen pulsing time from 0 to 20 s. It is found that the material band gap increases from 0.9 eV to 3.56 eV when the oxygen content increases. The deposited SnOx films exhibited improved and tuned optoelectronic properties, demonstrating their potential applications for developing alternative broadband photosensing layers and eco-friendly all-oxide solar cells. In this regard, SnOx-based photosensor and all-oxide SnOx solar cell structures were developed using the deposited thin-films. It is revealed that the prepared SnOx photosensor shows the highest responsivity of 32.7 mA/W and improved I-ON/I-OFF ratio of 48 dB. Moreover, the optimized all-oxide SnOx solar cell demonstrates a high efficiency of 3.41 %, a short-circuit current of 14.53 mA/cm(2) and an open circuit voltage of 0.49 V. These interesting results make the proposed elaboration process based on combined RGPP and GLAD techniques highly suitable for the development of high-performance optoelectronic and photovoltaic devices based on cost-effective metal oxide materials.

Our approach in this paper aims at investigating the impact of combining high-k dielectric materials with ferroelectric materials on the performance of silicon-germanium (SiGe) Junctionless Gate-All-Around (GAA) Field-Effect Transistors (SiGe-High-k-NCJLGAA-FETs). This offers unique advantage and can lead to a significant improvement in the device performance. The combination of high-k dielectrics and ferroelectric materials enhances the device electrical properties by providing enhanced channel electrostatic behavior. The electrical performance of the considered device (SiGe-High-k-NCJLGAA-FETs) is investigated by developing combined analytical and numerical models using ATLAS 2D simulator. The surface potential and subthreshold swing models, including short channel are developed by solving the Landau-Khalatnikov (L-K) equation with Poisson's equation. The effect of the Ge mole fraction on the device electrical performance is investigated. In addition, the influence of various high-k dielectric materials on the device performance is analyzed. A minimum swing factor (SS) of 31.2 mV/decade is recorded for L=30 nm using the doped-hafnium oxide as ferroelectric material and SiGe channel with TiO 2 gate oxide. These enhancements make the optimized transistor device highly approriate for usage in low-power and digital nanoelectronic applications.

In this work, the current-voltage (I-V) characteristics of nanoscale Junctionless Gate-All-Around (JLGAA) Field-Effect Transistor (FET) structures, with and without high-k dielectric materials, were investigated using the ATLAS 2D simulator. Various machine learning (ML) algorithms were employed to analyze and classify the design factors influencing one of the principal electrical parameters of the device, whixh is the subthreshold swing (SS) factor. The SS factor is a critical parameter in evaluating the performance of nanscale Junctionless GAA FE Ts, quantifying how effectively the transistor can switch from the off state (low current) to the on state (high current). Our approach aims to study and classify the impact of oxide thickness, channel radius, channe doping and high-k dielectric materials to better understund and enhance the gate control and achieve optimal performance. It was found that the proposed design can be classified into three categories based on SS values: Good SS ( $< 80$ mV/decade), Worst SS (80–500 mV/decade), and Extermly Worst-case SS ( $> 500$ mV/dec). This classification of SS values in 20 nm-Junctionless GAA FET provides a framework for understanding the trade-offs between performances and design parameters.

The current study investigates the use of Particle Swarm Optimization (PSO) for determining optimal parameters of the PID in the Field-Oriented Control (FOC) of a Dual-Star Linear Induction Motor (DSLIM). The objective is to enhance the motor’s performance by fine-tuning the PID controller settings. A mathematical model of the DSLIM and its FOC system is developed considering end-effect phenomenon, and an objective function is defined to evaluate the controller’s performance. The PSO algorithm is then employed to iteratively adjust the PID parameters based on the objective function. The effectiveness of the optimized PID parameters is validated through Matlab/ Simulink under various operating conditions. Results demonstrate improved motor performance in terms of speed regulation, torque control, and efficiency, highlighting the efficacy of PSO-based optimization in FOC of DSLIMs.

In this paper, we introduce an innovative design approach based on combined numerical simulations and machine learning (ML) analysis to investigate the design key parameters of ultra-low scale junctionless gate-all-around (JLGAA) field-effect transistor (FET) devices. To this end, precise 3D numerical models that incorporate quantum effects and ballistic transport are employed to simulate the current-voltage (I-V) characteristics of 10 nm-scale JLGAA FET devices. The influence of design parameter variations and high-k dielectric material on the subthreshold characteristics is thoroughly examined. Various ML algorithms were employed to analyze and classify the key design parameters influencing the subthreshold figures-of-merit (FoMs), the subthreshold swing (SS) factor and ION/IOFF ratio. The obtained results highlight that channel radius and channel doping design parameters are particularly important for affecting swing factor behavior. Similarly, these features also play a significant role in predicting and affecting ION/IOFF current ratio values. Additionally, machine learning is used to determine the optimal design parameters for each figure of merit (FoM) output value. In this context, the models effectively predicted both ION/IOFF current ratios and SS classification, with Naive Bayes achieving an accuracy of 90.8% for ION/IOFF and 92.6% for SS, showcasing the model's robustness in these classification tasks.

Lead-free perovskite solar cells (LFP SCs) emerged as potential alternatives for elaborating high-efficiency eco-friendly photovoltaic systems. However, further improvements in terms of light trapping optimization and short-circuit current should be developed to overcome the efficiency limitation. In this work, a design framework based on coupling plasmon-induced charge separation gold nanoparticles (Au-NPs) and light trapping engineering using back grooves is proposed, to enhance the photovoltaic performance of the CsSnI3 solar cell. Accurate numerical models based on combined Finite Difference Time Domain (FDTD)-SCAPS calculations are performed including the influence of Au-NPs and back grooves. In addition, particle swarm optimization (PSO) technique is used to boost up the absorption capabilities of the proposed CsSnI3 solar cell, where the best distribution of Au-NPs (radius = 38 nm, period = 365 nm) and geometry of back grooves (period = 183 nm, height = 76 nm, and width = 190 nm) are successfully selected. The recorded power conversion efficiency of the proposed CsSnI3 solar cell could achieve 5.75

The present study aims at analyzing the impact of new silver (Ag)/SnO2 thin-film structures, as alternative buffer layers, and graded bandgap (GBG) SnS absorber material on the photovoltaic properties of eco-friendly SnS thin-film solar cells (TFSCs). In this regard, structural, optical, and electrical characteristics of ultra-thin Ag/SnO2 bilayer structures were experimentally investigated using combined GLancing Angle Deposition (GLAD) and DC sputtering deposition techniques. The deposition angles of the particle flux were kept at 0° (planar) and 80° (oblique) during the DC sputtering deposition process. The influence of the Ag ultrathin top film and the deposition angles on the Ag/SnO2 buffer layer characteristics is investigated, where structure morphological and optoelectronic characterizations were performed using SEM, XRD, and UV–Visible spectroscopy measurements. The deposited ultra-thin Ag/SnO2 structures for different deposition angles showed Ag nanostructured layer (Ag-NL) with nanoparticles-like shape for oblique deposition. This demonstrates its potential application as an alternative electron transport layer (ETL) for efficient eco-friendly TFSCs. In this context, Ag-NL/SnO2/GBG-SnSSe solar cells were studied using the elaborated ETL structure. The recorded efficiency of Ag-NL/SnO2/GBG-SnSSe/Mo TFSC structure could reach 9.3