International Journal of Numerical Modelling-Electronic Networks Devices and Fields最新文献

This paper proposed analytical modeling of a Schottky tunnel field-effect transistor (STFET)—based biosensor with adjusted gate oxide. This model is developed by resolving the Poisson's equation and calculating the parabolic potential lateral to the channel depth. The special property of the bio-transistor, which serves as a biosensor, is then included in the analytical modeling of drain current. After the biomolecule interacts with the bio-transistor, a change in the drain current was employed as a metric to determine the sensitivity. The advanced analytical modeling explored several device restrictions. A device simulation is used to maintain and validate the established and planned characteristic trend. Consequently, the suggested model can be the right solution for the best design and fabrication of a biosensor.

A new, high-gain, high-efficiency, long-range passive metamaterial UHF-RFID tag is proposed. Measuring 16.1 × 8.1 × 1.6 cm, it integrates a modified taper-shaped dipole antenna with the NXP UCODE 8 chip and an artificial magnetic conductor (AMC) structure shaped like a double open-end wrench. This cost-effective tag achieves read ranges comparable to those of battery-assisted passive (BAP) tags, up to 64.7 m in a shipping container at 918 MHz and 56.7 m in free space at 928 MHz. Theoretical analysis shows moderate agreement with experimental validation in an anechoic chamber and outdoors. The proposed tag addresses challenging scenarios, enabling previously unattainable passive RFID applications. It supports vessel detection in maritime environments, with potential uses in automated access control at bridges, gates, and locks in smart waterways and premier marinas. It also supports multivehicle detection, allowing applications, such as streamlining emergency services and smart city traffic management. Furthermore, its capability extends to smart hangar applications, such as automatic updates to pilots' electronic logbooks and aircraft supervision.

This paper investigates the thermal operating capability of an interior permanent magnet synchronous machine. An iterative workflow is presented, combining electromagnetic modeling, control, power-loss modeling, and thermal modeling to identify maximum thermal stable operating points. Special attention is given to the critical temperatures of winding and permanent magnets. A nonlinear reduced order model based on finite element method model was used to simulate the system, including an inverter model with space vector pulse width modulation in combination with field-oriented control. Furthermore, an advanced iron core loss model and thermal lumped parameter model were employed. The presented approach allows for evaluating losses and their impact on steady-state temperatures. The obtained results highlight the significant influence of space vector pulse width modulation on iron core losses and the importance of considering both advanced power-loss models and adequate thermal models when analyzing the machine's thermal state. This research emphasizes the concept of a thermally stable envelope, providing a comprehensive understanding of the thermal boundaries under various operating conditions.

Power oscillations in modern power grids are inherent phenomena that may threaten system reliability. Therefore, to ensure acceptable system reliability, effective damping of power oscillations is inevitably required. In this context, this article introduces a novel approach to designing fractional power system stabilizer (FPSS) for effective damping of power oscillations. Bidirectional long-short-term memory (Bi-LSTM) approach is adopted to predict the parameters of FPSS. The conventional phase compensation technique is used to train Bi-LSTM network. To validate the efficacy of FPSS, different test scenarios of contingent operating conditions are simulated for the system. Comparative analysis is carried out with conventional power system stabilizers (PSSs) and optimization-based PSS techniques. Additionally, a test scenario is performed against existing deep neural network-based PSS methods to ascertain the robustness of the proposed PSS. Furthermore, the performance of the proposed Bi-LSTM-based FPSS is validated in real-time simulation using an interfaced OPAL-RT OP5700 hardware device.

In this article, the investigation of recessed-source/drain (Re-S/D) SOI feedback FET (FBFET)-based integrate and fire (IF) neuron circuit parameters is presented using a threshold switching device compact model. FBFETs offer high I_ON and low SS with minimal power consumption, operating efficiently at lower voltages and currents than conventional MOSFETs. Utilizing I_ON/I_OFF ratio and threshold voltage limits (V_t2/V_t1) of the device, a model is developed to mimic hysteresis characteristics, which is then used to implement an IF neuron circuit. Our findings show that altering the Re-S/D thickness between 0 and 50 nm enhances the I_ON of the device under study while decreasing hysteresis width. We detected a significant increase in output spike frequency of 46.8% and 65.14% for input current pulse amplitudes of 5 and 20 nA, respectively. Furthermore, increasing the Re-S/D thickness from 0 to 50 nm led to a significant 29.97% enhancement in spike amplitude. In addition, when using input current pulse amplitudes of 5 and 20 nA, we saw energy savings per spike of 3.36% and 12.7%, respectively. At the same time, there was an increase in power of 8.69% and 9.54%. These enhancements in performance metrics establish our proposed integrate and fire neuron circuit as a promising candidate for efficient neuromorphic system implementation.

The prime objective of this study is the simultaneous network reconfiguration with distributed generation (DG) and capacitor placement in radial distribution networks (RDN) to get the techno and economic benefits for two separate objectives, which are the minimization of actual power loss and annual economic loss as well as a multi objective combining these two single objectives using an oppositional arithmetic optimization algorithm (OAOA). It is an improved version of the currently suggested arithmetic optimization algorithm (AOA) used in the field of engineering for the optimization task. Though the recently developed AOA shows its efficacy in different optimization tasks but to improve the quality of solutions, convergence behavior, and to avoid the local optima, oppositional behavior is added to AOA. The efficacy and exactness of OAOA are tested on three test systems (33-bus, 69-bus, and 118-bus). For the reduction of power loss and annual economic loss as well as the multi objective optimization, two scenarios with different cases are executed using OAOA in RDNs. In scenario 1, the installation of the capacitor (case 1), the installation of unity power factor (UPF) based DG (case 2), and the placement of optimal power factor (OPF) based DG (case 3) have been executed. In scenario 2, allocation of UPF based DG and capacitors simultaneously (case 1), placement of OPF based DG and capacitors simultaneously (case 2) and simultaneous reconfiguration with installation of OPF based DG and capacitor (case 3) has been executed. This recommended OAOA algorithm provides the percentage improvement in real power loss and yearly economic loss for all cases of 33-bus, and 69-bus systems (34.28%, 65.50%, 94.43%, 93.26%, 94.89%, and 95.11%), (28.54%, 56.69%, 83.42%, 79.62%, 83.65%, and 83.71%), and (35.51%, 69.16%, 98.10%, 97.52%, 98.22%, and 98.25%), (30.26%, 61.68%, 88.75%, 85.42%, 88.81%, and 88.98%), respectively. The results and comparative study reveal that the OAOA is better than several optimization algorithms in terms of solution quality and good results. This algorithm has a good speed of response and convergence behavior.

The graphene nanoribbon field effect transistor abbreviated GNR-FET is seriously emphasized for the hydrogen gas detection owing to attractive properties induced by the graphene material. For the first time, a multiple-channel GNR deposited on 4H-SiC semiconducting material is offered to detect the hydrogen gas. The hydrogen gas by different pressures is released to the multiple-channel GNR to figure out the sensing power of the proposed sensor. The Pd metal is used as catalytic electrode trapping the hydrogen gas by making dipoles on the gate oxide/electrode interface. A Technology computer-aided design based model from the non-equilibrium green function (NEGF) method coupled with the Poisson–Schrodinger equation is used to simulate the electrical manner of the proposed gas sensor by workfunction modulation induced by these dipoles. The channel conduction during sensing hydrogen gas is much enhanced owing to the multiple-channel GNR configuration. Three sensitivity definitions based on threshold voltage, ON current, and OFF current are presented and applied as benchmarks to evaluate the sensing power of the gas sensor. The results have shown the domination of the multiple-channel GNR as compared to the single GNR sensor.

In this paper five enhancement-mode monolithically integrated white-light High electron mobility transistors-light emitting diodes (HEMT-LED) structures are proposed and simulated to obtain maximum light intensity, drain current I_d and maximum trans-conductance g_m. In first four HEMT-LED structures white light is generated by combining inbuilt yellow and blue lights and in fifth proposed structure the white light is generated with the combination of inbuilt red, green and blue lights. The InGaN quantum wells (QWs) are inserted in to e-mode ITO/p-GaN gate HEMT structures and the desired wavelength of light spectrums are generated by changing the in content (mole fraction), to obtain inbuilt white light. Among five proposed structures one shows Maximum I_d-max of 925 mA and maximum g_m of 250 mS, which is significantly higher than any HEMT-LED structures reported before. All the proposed structures are simulated in Silvaco TCAD software.