Current Voltage Research Articles

Study of doping effect of cd on ZrO2 nanostructures for gas sensing applications

Abstract Here, we enforced the conventional precipitation approach to yield pure and cadmium doped (3% and 8%) ZrO2 powder, to examine the gas sensing proficiency of Zirconium metal oxide. Drop casting mechanism is hired for the formation of pure and cadmium doped ZrO2 films. The X-ray diffraction (XRD) outcomes recommended that the prepared pure and cadmium doped ZrO2 powder samples are pure with no subordinate phase, but slight shift in peaks were detected. Scanning Electron Microscope (SEM) portrait substantiates the uniform spherical like morphology of the samples. The Current Voltage (I-V) study affirms the good conducting nature of the samples. The photoluminescence (PL) inspection, confirms that the oxygen vacant spots are present in the processed samples. The UV-Diffuse Reflectance Spectra validates the decrement of band gap of the prepared samples with rise in dopant concentration. The gas sensing capabilities of the drop casted samples were examined for different gas concentrations of reducing gases like ammonia, acetone, ethanol, formaldehyde and xylene. From the output data, we interpreted that the prepared samples show favorable response towards the target gas, explicitly the 8% cadmium doped sample shows 39% of an extreme response towards ammonia gas. Likewise, it also accomplishes an expeditious response time (RST) and recovery time (RCT) among all samples. Thus, we resolved that the inclusion of cadmium content stacks up the gas sensing adaptability of the zirconium metal oxide.

Coexistence of digital and analog resistive switching behaviors in nanocrystalline yttrium-iron-garnet thin films achieved by electrode engineering

Memristors with digital or analog resistive switching characteristics have the potential to acquire high-performance data storage or electronic synaptic device applications, respectively. It is considerably important to realize coexistence of digital and analog resistive switching characteristics for high-performance memristor applications. In this study, the nanocrystalline yttrium-iron-garnet Y3Fe5O12 (YIG) thin film was synthesized on Pt substrates by a facile solution-processed method, and it can be used as resistive switching layer for fabricating memory devices. The digital and analog resistive switching characteristics in YIG thin film devices were achieved by employing inert platinum and active silver electrodes, respectively. Oxygen vacancy-controlled conductive filaments (CFs) with Pt/YIG/Pt structure exhibited digital resistive switching behavior which can be modulated by changing voltage polarity. The resistive switching mode with positive Set/Reset in the Pt/YIG/Pt device acquired superior resistive switching properties. Ag/YIG/Pt device exhibited analog resistive switching characteristics based on the formation of Ag-based CFs. The conductance increase during Set operation and decrease during Reset process by adjusting current gradients and voltage windows, respectively. The present study exhibits an effective path to obtain the digital and analog resistive switching properties, which opens a door for understanding and optimizing resistive switching performance.

Multi‐Scale Simulation of Reverse‐Bias Breakdown in All‐Perovskite Tandem Photovoltaic Modules under Partial Shading Conditions

Herein, a multi‐scale simulation approach to quantify the impact of nonuniformities in cell‐level performance on the photovoltaic characteristics of monolithically interconnected large‐area all‐perovskite tandem modules under partial shading conditions is presented, addressing a crucial aspect of the up‐scaling challenge for this promising photovoltaic technology. To this end, current–voltage characteristics of small‐area all‐perovskite tandem solar cells are obtained for dark and illuminated cases from a calibrated optoelectronic device model using drift–diffusion simulation coupled to a quantum transport formalism for the band‐to‐band tunneling underlying the Zener breakdown. These current–voltage curves are computed for varying density of mobile ions and subsequently used as local 1D coupling laws connecting the 2D electrodes in a quasi‐3D large‐area finite‐element simulation approach that then provides the module characteristics under consideration of spatial variation in active area quality related to mobile ion density. The simulation reveals the appearance of localized current hot spots for the case where the shaded cell is strongly reverse biased.

Monitoring the gas metal arc additive manufacturing process using unsupervised machine learning

The study aimed to assess the performance of several unsupervised machine learning (ML) techniques in online anomaly (The term “anomaly” is used here to indicate a departure from expected process behavior which may indicate a quality issue which requires further investigation. The term “defect detection” has often been used previously but the specific imperfection is often indirectly inferred.) detection during surface tension transfer (STT)-based wire arc additive manufacturing. Recent advancements in quality monitoring for wire arc manufacturing were reviewed, followed by a comparison of unsupervised ML techniques using welding current and welding voltage data collected during a defect-free deposition process. Both time domain and frequency domain feature extraction techniques were applied and compared. Three analysis methodologies were adopted: ML algorithms such as isolation forest, local outlier factor, and one-class support vector machine. The results highlight that incorporating frequency analysis, such as fast Fourier transform (FFT) and discrete wavelet transform (DWT), for feature extraction based on general frequency response and defined bandwidth frequency response, significantly improves performance, reflected in a 14% increase in F2 score, compared with time-domain features extraction. Additionally, a deep learning approach employing a convolutional autoencoder (CAE) demonstrated superior performance by processing time-frequency domain data stored as spectrograms obtained through short-time Fourier transform (STFT) analysis. The CAE method outperformed frequency domain analysis and traditional ML approaches, achieving an additional 5% improvement in F2-score. Notably, the F2-score (The F2 score is the weighted harmonic mean of the precision and recall (given a threshold value). Unlike the F1 score, which gives equal weight to precision and recall, the F2 score gives more weight to recall than to precision.) increased significantly from 0.78 in time domain analysis to 0.895 in time-frequency analysis. The study emphasizes the potential of utilizing low-cost sensors to develop anomaly detection modules with enhanced accuracy. These findings underscore the importance of incorporating advanced data processing techniques in wire arc additive manufacturing for improved quality control and process optimization.

Wavelength‐Selective Near‐Infrared Organic Upconversion Detectors for Miniaturized Light Detection and Visualization

AbstractUpconversion detectors monolithically combining a detection unit and a light emitting unit, enables light detection and visualization in a compact structure, promising great advances in miniaturized multifunctional optoelectronics. The detection range of upconversion detectors usually covers a broadband spectrum, limiting their use in spectroscopic fields. This work investigates two wavelength‐selective upconversion detectors made with organic semiconductors to realize narrowband near‐infrared (NIR) light detection and visualization dual function. Two non‐fullerene‐based NIR‐sensitive bulk‐heterojunctions (BHJs) are exploited to make wavelength‐selective upconversion detectors, achieving peak sensitivity at 860 and 890 nm, with full width at half maximum of 125 and 170 nm, respectively. Each NIR‐sensitive BHJ comprises a donor polymer and a non‐fullerene acceptor, both of which are selectively sensitive to NIR light. The cumulative analysis of the optical properties of the absorber and current–voltage characteristics of the device indicates that the wavelength selectivity stems mainly from the wavelength‐dependent absorption. In particular, the upconversion detectors exhibit wavelength‐selective electronic and optical dual‐readouts, which are appealing for miniaturized spectroscopic applications, including health monitoring, optical communication, and microbead imaging, paving the way for practical applications.

Optimizing sensitizer concentration from senna tora leaves for enhanced interfacial interactions in dye-sensitized solar cells

A novel approach is adopted for the determination of the optimum concentration of natural dye powder extracted from the leaves of Senna Tora in the solvent ethanol and soak the TiO2 coated FTO substrate to be used as photoanode of dye sensitized solar cell (DSSC). Various concentration (0.5 g/15 ml, 0.75 g/15 ml, 1 g/15 ml and 1.5 g/15 ml) of the dye powder in ethanol is subjected for UV–Visible, FTIR and PL Spectroscopic studies. Solar cells are fabricated with the sensitizer in Photoanode, I-/I3- electrolyte and counter electrode of graphite coated FTO are subjected for JV characterization to determine the best concentration of dye to give good photoconversion efficiency. From the study, it is found that, among the four concentrations of the dye in ethanol for the extraction of the dye, 1.5 g in 15 ml of ethanol has shown higher efficiency of 2.29 % with fill factor, short circuit current density and open circuit voltage of 0.84, 2.64 mA/cm2 and 1.03 V.

Enhancing DC microgrids cluster performance with distributed event-triggered consensus protocols

Microgrid (MG) clusters offer solutions to the challenges faced by individual MGs in extreme conditions but require precise coordination to manage generation-load imbalances effectively. This paper presents a hierarchical control strategy that combines time-based and event-based consensus protocols (ETC) in the overarching control layer. Traditional time-based protocols require constant sampling, leading to unnecessary communication and computational burdens. To address this, we introduce a mixed time-state dependent distributed event-triggered consensus protocol (MDETC), which reduces communication needs while maintaining MG performance. A set-time consensus algorithm is used at the secondary control level to synchronize currents and voltages quickly. Additionally, the integration of Particle Swarm Optimization (PSO) in primary control ensures efficient current distribution and voltage regulation among neighboring MGs. The study also explores automatic reconfiguration within the cluster to support Plug-N-Play functionality, ensuring seamless MG operation. The effectiveness of this control approach is demonstrated through MATLAB simulations of a four-DC MG cluster, showing improved efficiency in reducing triggering instances and achieving faster current and voltage stabilization under various conditions.

DUAL LOOP VOLTAGE DROOP REGULATED CONTROLLER FOR DC MICROGRID USING HYBRID PSO AND GGO ALGORITHMS

Abstract DC microgrids are effectively employed in distribution network integration with renewable energy sources. The droop control technique is commonly utilized for DC microgrid regulation during load sharing. It minimizes the potential instability caused by disturbances such as input voltage changes, uncertainty parameters, and constant power load (CPL). Nevertheless, conventional droop control is inadequate in achieving precise current and satisfactory voltage regulation distribution for load sharing. The proposed approach comprises two separate loops for the DC bus voltage regulation and load current distribution, delivering a CVL and CPL to overcome the single optimization voltage controller technique. The primary loop uses the PSO algorithm to precisely manage the current load sharing among two parallel converters. Due to this the instability problems arising from disturbances, and it mitigated by the proposed topology. The multiobjective optimization technique provides notable resilience, rapid dynamic response, and robust stability over considerable variations in load. The secondary loop utilises the GGO algorithm to optimize the parameter to minimize the DC bus voltage regulation, voltage management, reasonable distribution of load power, and suitable reliability. The performance of the voltage regulation enhance & settling time for output voltage has been mitigated more than 31ms times through the proposed controller compared to conventional controller under the variation of CVL, CPL, and input voltage disturbance demonstrated in simulation results.

Enhancing Multi-Junction Solar Cell Performance: Advanced Predictive Modeling and Cutting-Edge CIGS Integration Techniques

Historically, multi-junction solar cells have evolved to capture a broader spectrum of sunlight, significantly enhancing efficiency beyond conventional solar technologies. In this study, we utilized Silvaco TCAD tools to optimize a five-junction solar cell composed of AlInP, AlGaInP, AlGaInAs, GaInP, GaAs, InGaAs, and Ge, drawing on advancements documented in the literature. Our research focused on optimizing these cells through sophisticated statistical modeling and material innovation, particularly examining the relationship between layer thickness and electrical yield under one sun illumination. Employing III-V tandem solar cells, renowned for their superior efficiency in converting sunlight to electricity, we applied advanced statistical models to a reference solar cell configured with predefined layer thicknesses. Our analysis revealed significant positive correlations between layer thickness and electrical performance, with correlation coefficients (R2 values) impressively ranging from 0.86 to 0.96 across different regions. This detailed statistical insight led to an improvement in overall cell efficiency to 44.2. A key innovation in our approach was replacing the traditional germanium (Ge) substrate with Copper Indium Gallium Selenide (CIGS), known for its adjustable bandgap and superior absorption of long-wavelength photons. This strategic modification not only broadened the absorption spectrum but also elevated the overall cell efficiency to 47%. Additionally, the optimization process involved simulations using predictive profilers and Silvaco Atlas tools, which systematically assessed various configurations for their spectral absorption and current–voltage characteristics, further enhancing the cell’s performance. These findings underscore the critical role of precise material engineering and sophisticated statistical analyses in advancing solar cell technology, setting new efficiency benchmarks, and driving further developments in the field.

Lattice reconstruction for mixed-halide blue perovskite light-emitting diodes with high brightness, outstanding color stability and low efficiency roll-off

We report a simple, effective, and universal lattice reconstruction approach to improve the quality of perovskite films by using nonpolar solvents with high Gutmann donor numbers (DNs). We find that high-DN nonpolar solvents, for instance, ethyl acetate, can interact with perovskite precursors. Such a solvent can make the perovskite lattice more ordered and “harder” and promote the formation of heterostructures with low-dimensional perovskite impurities and residual solvent molecules. As a result, the lattice-reconstructed perovskite films exhibit reduced defect densities and suppressed ion migration. The resultant mixed-halide blue perovskite light-emitting diodes (PeLEDs) show greatly enhanced tolerance to high driving current densities and voltages, demonstrating high brightness, outstanding color stability and low efficiency roll-off. Our work provides a deep understanding of the interactions between nonpolar solvents and perovskites and offers useful guidelines for further development of high-power PeLEDs.

A Joint Active Damping Strategy Based on LCL-Type Grid-Connected Inverters for Grid Current Feedback and PCC Voltage Unit Feedforward.

The negative high-pass filter feedback of the grid current (NFGCF) can offer active damping for the LCL-type grid-connected inverter. Due to the control delay in digital control systems, this damping can cause the system to exhibit non-minimum phase behavior within specific frequency ranges. This study proposes a joint active damping approach that combines grid current feedback and the point of common coupling (PCC) voltage unit feedforward. The proposed method introduces a dynamic damping region that varies with grid impedance. By developing suitable damping loop control parameters, this region can span the entire frequency range, even exceeding the Nyquist frequency fs/2. The research results demonstrate that the proposed approach enhances robustness against variations in grid impedance and eliminates non-minimum phase behavior. Simulation and experimental outcomes validate the effectiveness of this joint active damping method.

Sustainable EDM production of micro-textured die-surfaces: Modeling and optimizing the process using machine learning techniques

The present research explores the role of die-sinking EDM parameters, such as peak current, pulse duration, and gap voltage, and their proper selection for the cost-effective fabrication of negative micro-pillar-textured surfaces using a die-material of H13 steel alloy. The developed artificial neural networks (ANN) models of surface roughness and overcut outperform the response surface methodology models in predicting responses as higher ‘R2 values’ and lower ‘mean squared error’ with ANN models. A lower peak current of 2 A, lower pulse duration of 55.46 µs, and intermediate gap voltage of 37.83 V is selected from metaheuristic optimization approaches such as teaching–learning-based optimization and particle swarm optimization that are efficient and comparable with the desirability function-based approach while finding optimum parameter values. Further, sustainable fabrication of micro-textured H13 die surfaces is carried out on large areas using selected optimized parameters with a form tool electrode. The negative micro-pillar pattern surface demonstrates an average overcut of ∼ 40 ± 15 µm in comparison to the dimensions of the form tool. This research emphasized the sustainability of the EDM process by utilizing reusable dielectric fluid, prioritizing optimal parameters for high-quality fabrication using low electrical-energy utilization, and enabling large-scale production using a form tool.

Feed Forward Modeling: an efficient approach for mathematical modeling of the force frequency relationship in the rabbit isolated ventricular myocyte

Background and Objective. This study addresses the Force-Frequency relationship, a fundamental characteristic of cardiac muscle influenced by β 1-adrenergic stimulation. This relationship reveals that heart rate (HR) changes at the sinoatrial node lead to alterations in ventricular cell contractility, increasing the force and decreasing relaxation time for higher beat rates. Traditional models lacking this relationship offer an incomplete physiological depiction, impacting the interpretation of in silico experiment results. To improve this, we propose a new mathematical model for ventricular myocytes, named ‘Feed Forward Modeling’ (FFM). Methods. FFM adjusts model parameters like channel conductance and Ca2+ pump affinity according to stimulation frequency, in contrast to fixed parameter values. An empirical sigmoid curve guided the adaptation of each parameter, integrated into a rabbit ventricular cell electromechanical model. Model validation was achieved by comparing simulated data with experimental current–voltage (I-V) curves for L-type Calcium and slow Potassium currents. Results. FFM-enhanced simulations align more closely with physiological behaviors, accurately reflecting inotropic and lusitropic responses. For instance, action potential duration at 90% repolarization (APD90) decreased from 206 ms at 1 Hz to 173 ms at 4 Hz using FFM, contrary to the conventional model, where APD90 increased, limiting high-frequency heartbeats. Peak force also showed an increase with FFM, from 8.5 mN mm−2 at 1 Hz to 11.9 mN mm−2 at 4 Hz, while it barely changed without FFM. Relaxation time at 50% of maximum force (t50) similarly improved, dropping from 114 ms at 1 Hz to 75.9 ms at 4 Hz with FFM, a change not observed without the model. Conclusion. The FFM approach offers computational efficiency, bypassing the need to model all beta-adrenergic pathways, thus facilitating large-scale simulations. The study recommends that frequency change experiments include fractional dosing of isoproterenol to better replicate heart conditions in vivo.

Formation of Conjugated Polymer Monolayer Networks on Water Surface and Nonlinear Charge Transport

AbstractMaterial‐networked conduction paths provide nonlinear electronic properties, which are essential components of computing and physically mimic the brain. In this study, the formation of conjugated polymer monolayer networks and their nonlinear charge transport is demonstrated. Poly(3‐hexylthiophene) (P3HT) monolayer networks doped with 2,3,5,6‐tetrafluoro‐7,7,8,8‐tetracyanoquinodimethane (F4TCNQ) is fabricated using the co‐spread method with an amphiphilic liquid crystal molecule at the air–water interface. Atomic force microscopy and Ultraviolet–visible–near‐infrared absorption spectroscopy measurements reveal the network surface morphologies and doped electronic states. The correlation between the nonlinear electronic characteristics and network structures of the P3HT/F4TCNQ monolayer networks is further systematically investigated through current–voltage and voltage–time measurements for various doping levels, network densities, and numbers of transferred layers. The current–voltage characteristics of the P3HT/F4TCNQ monolayer network device with a simple two‐terminal structure exhibit nonlinear and ohmic conduction behavior, which depend strongly on the network density and geometric dimension (number of transferred layers). It is concluded that the nonlinear properties arise from the limited and unique network of 2D conduction passes. This study highlights the unique features of conducting polymer monolayer networks, paving the way for neuromorphic device applications including conjugated semiconducting polymer‐based material reservoirs with controllable nanostructures.

Airfoil cross flow field to enhance mass transfer capacity and performance for PEMFC

Proper design of the flow field in bipolar plates is important for improving proton exchange membrane fuel cells in water transport, gas distribution and net power density enhancement. Inspired by the fact that the streamlined design of an airplane airfoil makes the flow resistance reduced, a new airfoil cross flow field is proposed. Airfoil cross flow field is composed of a regular pattern of airfoil pins which are categorized in pin-type flow fields. The effects of different airfoil-pin shapes and arrangements on cell performance were explored. The results showed that airfoil cross flow field improved water management by finely modulating the airflow significantly increasing the gas flow rate in the channel. In addition, the airfoil cross flow field design achieves higher and more uniform oxygen distribution at the interface between the gas diffusion layer and the catalyst layer. The proper shape and arrangement of the airfoil-pins can effectively increase the net power density of the cell by 10.65 % and 2.49 % compared to the conventional parallel flow field and the square-pin flow field. Due to the streamlined design of the airfoil cross flow field, the voltage drop is approximately 60 % of that of the conventional parallel flow field and even slightly lower than that of the square-pin flow field, which demonstrates its high practicality. In summary, the airfoil cross flow field well coordinates the high current density and low voltage drop requirements of proton exchange membrane fuel cells, and some new points of view on flow field design are presented.

MOS-structured MoS2/GaN Schottky barrier diodes with high on/off current ratio and low threshold voltage

GaN Schottky barrier diodes (SBDs) have been investigated for a variety of power applications. However, the problems of low on/off current ratio and high threshold voltage caused by the difficult high-quality doping restrict its utilization in power devices. In this work, quasi-vertical MoS2/GaN SBDs with Metal-Oxide-Semiconductor (MOS) structure have been proposed. The MOS structure is formed by the part of anode (Ni/Au) over the Al2O3, Al2O3, and monolayer MoS2. Monolayer MoS2 exhibits n-type doping and p-type doping under forward bias and reverse bias, respectively, which realizes an adjustment in the Fermi level of the monolayer MoS2, resulting in a change in the resistance of the SBDs. The as-prepared SBDs present a high on/off current ratio of 2.40 × 1011, a low threshold voltage of 0.55 V at 1 A/cm2, and a low reverse leakage current of 2.50 × 10−10 A/cm2 at −1 V. This work shows that the MOS-structured MoS2/GaN SBDs are promising for next-generation power electronics.

Memristive behaviour of Al/rGO-CdS/FTO device at different temperatures: A MATLAB-integrated study

In the present work, a comprehensive study is carried out to investigate the memristive behaviour of reduced graphene oxide (rGO) conjugated cadmium sulphide quantum dot (CdS QD) nanocomposites (rGO-CdS), offering insights into their dynamic response under varying thermal conditions. The study integrates experimental analysis with MATLAB simulation to provide a detailed understanding of the complex interplay between different operating temperature (300K, 350K, 400K, and 450K) and memristive behavior in rGO-CdS nanocomposites. Different structural and chemical characterizations were carried out which confirms the formation of rGO-CdS nanocomposites. A sandwich structured device was fabricated with the synthesized rGO-CdS nanocomposites using Aluminum (Al) as top and Fluorine doped tin oxide (FTO) as bottom electrode. The influence of operating temperature on hysteresis behaviour of the fabricated Al/rGO-CdS/FTO device was investigated using Keithley 2450 source meter by sweeping a direct current (dc) voltage (−5 V → 5 V → −5 V). Notably, we observe a positive temperature coefficient in the device current, with maximum and minimum recorded current of |1.29mA| and |0.66mA| at 450K and 300K respectively. The current-voltage (I-V) behavior observed in the device reveals that in the low resistance state (LRS), conduction is dominated by bulk-limited mechanisms. However, in the high resistance state (HRS), conduction involves contributions from both Schottky barriers and the Pool-Frenkel effect. A MATLAB based linear drift model was used to simulate the device responses at different temperatures using the experimental data. The study provides first comprehensive analysis of temperature dependent hysteresis behaviour of Al/rGO-CdS/FTO device, integrating MATLAB simulation to glean valuable insights into its operation and possible applications as memristive material across different temperature regimes.

Thickness dependence and crystallization properties of amorphous GeTe thin films on silicon dioxide

Radio frequency magnetron sputtering was used to prepare the amorphous GeTe thin films on silicon dioxide and the thickness effects on the crystallization behavior were investigated. With the film thickness reducing, the crystallization temperature, crystallization activation energy, amorphous and crystalline resistance increase remarkably, indicating the great improvement in thermal stability and power consumption. Ozawa’s model was used to estimate the crystallization kinetics of GeTe thin films, it shows that nucleation and grain growth occur simultaneously, and grain growth dominates ultimately. XRD analysis demonstrated that the grain size can be reduced and the crystallization process of GeTe thin film can be inhibited with the film thickness decreasing. Furthermore, the thinner film has smaller resistance drift index and surface roughness, which are beneficial to improve the reliability of storage device. T-type phase change memory devices based on 25 nm GeTe thin film were fabricated by 0.13 μm CMOS technology, and the current–voltage and resistance-voltage characteristics demonstrate the excellent electrical performance, including the fast resistance switching between SET and RESET processes, low threshold current and voltage. All the results proved the strong dependency relationships between the crystallization properties and film thickness of GeTe thin film, which paves the way for developing high-density phase change memory in the fields of big data and artificial intelligence.

Resonant oscillation of droplets under an alternating electric field to enhance solute diffusion

This study investigates a novel microfluidic mixing technique that uses the resonant oscillation of coalescent droplets. During the vertical contact-separation process, solutes are initially separated as a result of the combined effects of diffusion and gravity. We show that the application of alternating current (AC) voltage to microelectrodes below the droplets causes a resonant oscillation, which enhances the even distribution of the solute. The difference in concentration between the top and bottom droplets exhibits frequency dependence and indicates the existence of a particular AC frequency that results in a homogeneous concentration. This frequency corresponds to the resonance frequency of the droplet oscillation that is determined using particle tracking velocimetry. To understand the mixing process, a phenomenological model based on the equilibrium between surface tension, viscosity, and electrostatic force was developed. This model accurately predicted the resonance frequency of droplet flow and was consistent with the experimental results. These results suggest that the resonant oscillation of droplets driven by AC voltage significantly enhances the diffusion of solutes, which is an effective approach to microfluid mixing.

Integration of artificial intelligence with a customized Four-Probe station for [formula omitted] characteristic classification and prediction

The incorporation of Artificial Intelligence (AI) is pivotal in automating intricate technical tasks, significantly enhancing accuracy and efficiency while alleviating the burdens of repetitive monitoring traditionally borne by technicians. This study focuses on developing a customized four-probe station integrated with sophisticated AI models aimed at classifying current–voltage (I-V) characteristics and extracting essential parameters. Our methodology encompasses the fabrication of precision-engineered gold-plated probes, meticulously assembled with a three-dimensional (3D) moving head to ensure optimal contact and measurement fidelity across a variety of electronic and optoelectronic devices. Data acquisition is executed via a source meter unit, followed by rigorous post-processing utilizing advanced algorithms, including convolutional neural networks and random forest techniques. Notably, the gold-plated contacts enhance measurement accuracy by providing superior conductivity and minimizing contact resistance, while the movable head allows for dynamic adjustment, facilitating precise probe alignment for consistent data retrieval. The results demonstrate a remarkable capability in classifying I-V characteristics with a root-mean-square (RMS) error of less than 1%, underscoring the system’s reliability and accuracy. Moreover, our predictive models effectively utilize previously recorded measurements to forecast the degradation profiles of devices, thus offering significant insights into device longevity and performance.