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

LPI radar signal recognition based on convolutional neural networks usually assumes that the signal to be recognized belongs to a closed set of known signal classes. In an open electromagnetic signal environment, this type of closed-set recognition method will experience a drastic drop in performance due to the encounter with unknown types of signals. We propose an SCNN-SVDD model based on a combination of a lightweight convolutional neural network and a support vector data description algorithm to achieve open-set recognition of LPI radar signals under unknown signal conditions. In this approach, Choi-William's time-frequency distribution is used to obtain two-dimensional time-frequency images of the signal to be identified, and convolutional neural networks are used to achieve high-precision classification of known signals and extract the corresponding feature vectors. Then, the feature vectors are used as input to the SVDD algorithm and a hypersphere is constructed to detect whether the signal to be identified belongs to a known class. Experimental results show that the proposed method can detect unknown signals while maintaining high recognition accuracy for known signals.

In a recent paper, it has been shown that, if an N-port network fulfills the condition of (geometrical) unconditional stability at a given frequency, then its scattering parameters will also necessarily satisfy N easily computable bounds, one per port. In order to complete that picture, this contribution investigates whether a tighter bound can be obtained by combining the $��N�$ bounds into just one. The answer is in general negative, except that the 3-port case does indeed exhibit a peculiar behavior: this can be exploited to reduce the upper bound when the diagonal elements of the scattering matrix are limited in magnitude up to some $��\alpha �$, and in particular for $��\alpha �=�0�$ (simultaneous conjugate match).

In this work, we have presented an analytical model for a recently proposed symmetrical recessed double gate junctionless field-effect transistor (R_DGJLFET) operating in subthreshold region. The model has been developed by solving a two-dimensional Poisson's equation in three continuous rectangular silicon regions, resulting in explicit expressions for surface potential, center potential, and eventually the subthreshold drain current. The model provides deeper physical insights by successfully incorporating the effect of various design parameters such as doping concentration, gate length, gate work function, and effective oxide thickness on the subthreshold drain current. Moreover, the effect of variations in gate length on the subthreshold slope has also been presented. The model results have been validated with the simulation results obtained from the Silvaco Atlas tool and found reasonably close.

Low-noise and low-voltage operation is prime requirement of an operational transconductance amplifier for low frequency applications. However, achieving low-noise operation at low supply voltages is a challenging task in CMOS technology owing to noise-power and noise-stability tradeoffs. This article outlines, the design of four differential bias self-cascode (DBSC) operational transconductance amplifiers (OTAs) working at ±0.7 V. The four design techniques namely gate driven (GD), bulk driven (BD), bulk driven quasi-floating gate (BDQFG), and gate driven quasi floating bulk (GDQFB) have been applied on DBSC OTAs. The designing aspects and performance parameters of these four OTAs such as gain, gain-bandwidth, input referred noise (IRN), settling time (ST), common mode rejection ratio (CMRR), total harmonic distortion, input impedance, transconductance, power consumption, area consumption and process/mismatch variations have been fairly compared in this work. These DBSC OTAs have been designed and simulated using a standard 0.18-μm 6M1P CMOS N-well process. The results infer GD DBSC OTA shows high CMRR of 125.83 dB. While the BD DBSC OTA consumes very low power of 0.2 μW. The BDQFG DBSC OTA shows low 1% ST of 24.83 μS. The GDQFB DBSC OTA show high transconductance (2.35 mS), high gain (64.97 dB), and low IRN (0.40 μV/√Hz at 10 Hz). The theoretical predictions for these OTAs agree with the post-layout simulations. The proposed OTAs can be used for designing various analog circuits such as programmable gain amplifiers, variable gain amplifiers, and transimpedance amplifiers for low-frequency biomedical and health care applications.

In recent years, gallium nitride (GaN) high electron mobility transistors (HEMTs) have come to the forefront of the semiconductor industry because of their exceptional performance in both high-power and high-frequency utility. Accurate capacitance modeling is crucial to optimize performance and facilitate energy-efficient electronic circuit design. In order to reflect the complex nature of the aluminum scandium nitride (AlScN) gate capacitance in GaN HEMTs this study investigates the use of the unique Grünwald-Letnikov model based on fractional order calculus. The proposed model presents a powerful approach to accurately characterize capacitance since fractional order derivatives allow modeling of non-integer order systems. Quantitative assessment of the Grünwald-Letnikov model's accuracy is performed through various error metrics, including mean absolute error (MAE), root mean square error (RMSE), maximum percentage error (MPE), mean absolute percentage error (MAPE), and mean squared error (MSE), by comparing the model's predictions to experimental data. Notably, this model demonstrates remarkable consistency in error metrics, with maximum values of MPE = 0.21%, MAE = 0.05%, MAPE = 0.33%, MSE = 0.01%, and RMSE = 0.09% for the forward scan, and MPE = 0.32%, MAE = 0.04%, MAPE = 0.39%, MSE = 0.01%, and RMSE = 0.08% for the backward scan. These metrics affirm the model's precision in capturing the nuanced capacitance characteristics of GaN HEMT devices. Hence, herein for the first time, the novel Grünwald-Letnikov model, augmented by fractional order calculus, proves to be a robust tool for accurately characterizing GaN HEMT capacitance. Its ability to seamlessly account for the complexities introduced by using ferroelectric material highlights its potential for advancing semiconductor design and optimizing device performance.

This paper presents a Computer-aided design (CAD) model for a-IGZO thin film transistors (TFTs) by adapting SPICE level-61 RPI a-Si: H (Hydrogenated Amorphous Silicon) TFT model and level-62 RPI Poly-Si (Poly Silicon) TFT model. This work provides a complete understanding of SPICE level-61 and 62 model parameters, which must be tuned for a-IGZO TFT simulation. The adapted SPICE models of level-61 and level-62 could model all regions of operation of the TFT, that is, above-threshold and below-threshold regions. Adapted RPI poly-Si model also shows the kink effect in ZnO thin film transistors (TFTs) due to the recombination of electron–hole pairs in the channel via boundary trap states present in the poly-Si TFTs thereby increasing the drain current in the transistors above pinch-off region. The extracted performance parameters of the adapted models were found to be contiguous with experimental results. The maximum deviation in the subthreshold slope is approximately 5 mV/decade for level-61 a-Si TFT, and for level-62 poly-Si TFT, deviation in the subthreshold slope is even less, that is, 0.2 mV/decade. The experimental and simulated characteristics, extracted on-to-off ratio, negative bias reverse saturation current, and threshold voltage were almost similar. However, an average deviation of 2.4% and 2.27% was observed in the output characteristics of the adapted level-61 and level-62 models, respectively.

This study presents a new dual-metal dopingless tunnel field effect transistor with a Germanium source (GeS-DM-DLT) for label-free biomolecule detection. Introducing a Ge source and dual-metal gate provides improved drain current. We have considered an L-shaped cavity at the top and bottom source metal region for investigating the sensitivity. The biosensor's sensitivity has been measured using the neutral biomolecules' dielectric constants (varying the k-values in the cavity). The sensor's DC performance is investigated using transfer characteristics, BTBT rate, energy band, and electric field variation for different k-values. The sensitivity performance of the proposed biosensor is evaluated in terms of different DC parameters (drain current, surface potential, subthreshold swing, interband tunneling rate, electric field) and RF parameters (parasitic capacitance, transconductance, cut-off frequency, maximum frequency). The suggested biosensor offers a much-improved S_ON of 9.86 × 10⁸ and S_RATIO of 1.94 × 10⁴ for a dielectric constant of 22.0 at room temperature. Further research has been done to study the effects of dielectric materials, interface trap carriers (ITC), and temperature on drain current, drain current sensitivity, and other sensitivity parameters. The article also includes investigating the influence of the fill factor on sensitivity performance. The GeS-DM-DLT sensor performs best in fully-filled conditions compared to the partially-filled condition inside the cavity region.

In this paper, an automatic multi-objective particle swarm optimization (AMOPSO) method is developed for the design of Doherty power amplifiers (DPAs). In comparison to the well-known built-in optimizer available in commercial simulators, the proposed method not only reduces the optimization time, but also provides superior power added efficiency (PAE) for the final power amplifier. According to the reported measurements, the output PAE of the fabricated DPA exceeds 50% at 6 dB backoff, the saturated PAE is more than 61%, and the saturated output power (Pout) is over 43 dBm in the target frequency range of 1.9–2.1 GHz. As compared with existing optimization methods, the proposed method allows reducing optimization time by more than 37%.

This paper presents a novel poly-harmonic distortion (PHD) model that incorporates the DC input and output bias voltages using Gaussian process regression (GPR). Simulation tests were conducted using a 10-W gallium nitride (GaN) HEMT transistor from Wolfspeed, and the model implementation test was performed in the Keysight Advanced Design System environment. The results showed that the GPR-based PHD model exhibited good performance in predicting both fundamental and harmonic behaviors over a wide range of bias variations with significant advantages over basic linear regression methods. Additionally, the model accurately predicted load-pull simulations. The measurement test was conducted using a 6-W GaN device, and the results showed a mean error of 2.22% and 4.54% for the fundamental and second harmonic of the reflected wave, respectively.

This work introduces a novel data-driven model-free modified nodal analysis (MNA) circuit solver. The solver is capable of handling circuit problems featuring elements for which solely measurement data are available. Rather than utilizing hard-coded phenomenological model representations, the data-driven MNA solver reformulates the circuit problem such that the solution is found by minimizing the distance between circuit states that fulfill Kirchhoff's laws, and states belonging to the measurement data. In this way, the formerly inevitable demand for model representations is eliminated, thus avoiding the introduction of related modeling errors and uncertainties. The proposed solver is applied to linear and nonlinear RC-circuits and to a half-wave rectifier.