مجله مهندسی برق و الکترونیک ایران
سال نوزدهم شماره 2 (تابستان 1401)

High actuation voltage is one of the most important limitations of RF MEMS switches. One way to reduce the actuation voltage is to reduce the spring constant in the switch structure.  In this paper, we model the spring constant using Energy Model and we obtain an analytic equation to calculate it. The result is an analytic design guide that determines the minimum value of the spring constant. Then, a new RF MEMS structure with very low actuation voltage is presented. The structure is modeled by the energy method and the spring constant is calculated analytically. Analysis results show the spring constant of 0.0714 N/m and an actuation voltage of 1.61 V. To evaluate the performance of the proposed structure and to validate the results of the analysis, a switch was designed using simulated COMSOL software. The results of this simulation show the actuation voltage of 1.8 volts, the switching time of 25.6 microseconds, the 4.5 MPa Von Mises stress, the natural frequency of 3118.6 Hz, its mass of 0.206 ng and finally the spring constant of 0.079 N/m. The beam is made of gold and Si3N4 is used as a dielectric. Finally, the high frequency performance of the switch is designed using simulated HFSS software. The simulation shows a 25 dB isolation, a 0.7 dB insertion loss, and a 16 dB return loss at 10 GHz.

SPDT switches are used as duplexer in wireless systems, as WIMAX, to switch between transmit and receive states. Microwave SPDT switches may be designed using FET’s or PIN diodes. However, PIN diode is suitable for duplexer applications due to its high power handling capability. I general, in frequencies higher than 3 GHz, isolation better than 30 dB is a challenge. This is due to the parasitic inductance of PIN diode.In this paper, we propose a new structure for SDPT PIN diode switch to overcome this challenge. In the proposed structure a micro-strip line is adopted parallel to the diode. This line, in conjunction with another micro-strip line that is in series with the diode, eliminates the diode inductance in on-state and the diode capacitance in off-state, and hence improves both of isolation and insertion loss. The proposed structure has been analyzed mathematically and analytic equations have been derived to design the proposed switch. Finally, simulation results have been used to validate the performance of the switch. Simulations are performed using SMP1340 PIN diode and the model presented by the diode manufacturer. The simulations are in ADS and EM and EM_Cosim simulators are used to simulate the switch layout and include the layout effects accurately. Simulation results show more than 50dB isolation and less than 0.2dB insertion loss.

Performance of the current steering digital to analog converters are limited by transistors channel width and length mismatches and their Threshold and Early voltage variations due to fabrication process errors. Although there are several ways to reduce errors due to element mismatches, however these errors cannot be completely eliminated. In this paper, Distributed MOS Transistor Technique is utilized which facilitates Dynamic Element Matching implementation capability in Binary Digital to Analog Converter. The proposed technique reduces the errors due to element mismatches and also load voltage variations needless of high power consumption and complex circuitry. This technique operates based on random selection of unit current blocks among specific number of available current units. To make the generated code as random as possible, a random code generator, full adder and 4*16 decoder have been used. This technique is realized in a 10-bit digital to analog converter with 180 nm CMOS technology. The LSB current is 500nA and supply voltage is 1.8v and the power consumption of this converter is 14.6 mW and SFDR of DAC is achieved 60.27 dB based on simulation result with Cadence Spectre software.

In this paper by using a nanowire transistor, a new ternary inverter gate is designed. The core of this design, is a nanowire transistor with a (7nm×7nm) cross-sectional area surrounded by silicon dioxide and there are three separated gates on that oxide. By using this design, all three parts of ternary inverter include standard ternary inverter (STI), negative ternary inverter(NTI), and positive ternary inverter (PTI) are implemented by one circuit and without hardware change. The type of inverter function can be specified by the control gate voltage level. A simulation method is based on solving self-consistent Schrodinger-Poisson equations. By using this method, static power and noise margins have been calculated. The simulation results indicate that the noise margins have improved a lot compared to previous designs, but the static power remains the same.

Quantum-dot cellular automaton is a new technology for implementation of logic gates and digital circuits at nanoscale. By reducing size of the components, sensitivity of circuit increases and quantum circuits become more vulnerable to adverse environmental factors. Due to the importance of designing fault tolerant circuits, this paper presents a five-input majority gate with fault tolerance characteristic in quantum cellular automata technology. All possible faults which may occur during the process of placing cells in specific locations on the surface including displacement, deletion, rotation, and extra cell are evaluated.  In the first step, all the four types of faults are applied to the gate and the next step is to evaluate the accuracy of the gate performance with the QCADesigner simulator. In order to find such a majority gate, different methods, including the method of adding cells (the injection of redundancy to the gate) and the specific arrangement of cells are utilized. The latter method is being used more, hence, low overhead is added to the design.  The results confirm the superiority of the proposed gate over the other previously offered designs.

In this paper, an example of a silicon telecommunication modulator based on micro-ring Resonator is designed. In order to achieve high bandwidth, the coupling coefficient must be improved. This issue is investigated by changing the coupling coefficient for different coupling lengths and gaps between the bass waveguide and the ring. The optimal coupling length was calculated. By numerical calculation, the impurity and carrier distribution in a PN phase modulator are investigated and various effects such as doping distance change, doping rate change and its effect on refractive index and absorption changes are investigated. By improving the modulator parameters and optimizing the coupling coefficient of the ring modulator the DC gain is enhanced and the electro-optical response became more stable. Comparisons have also been made to similar modulators and bandwidth has been improved up to one order.

In optoelectronic devices such as photodetectors, solar cells, modulators, filters, etc., the optical absorption coefficient is a key parameter in successful device operation. To this end, in this paper, a dual band absorber, consisting of a metal-graphene-dielectric-metal structure which can operate at communication bands is presented. First of all, the light absorption characteristic has been achieved by finite difference time domain method and then the influences of the thicknesses of top metallic structure, dielectric and substrate layers on the light absorption of the structure have been analyzed. The results revealed that utilizing the graphene monolayer and the plasmonic structure improves the light-graphene interaction and in result, the graphene layer absorption. It is shown that the proposed structure has the light absorption of 95% at the wavelength of 1310nm and the light absorption of 96% at the wavelength of 1675nm. Therefore, this absorber has the ability of operating at two optical communication bands (O-band and U-band) simultaneously.

There are two important issues in the design of optical devices and in special in optical couplers; down scaling and loss reduction. In this paper, a nonlinear plasmonic directional coupler consisting of two MIM (metal- insulator- metal) waveguides is proposed. In order to reduce the coupling length, part of the waveguides structure is designed in the shape of a trapezoid. Numerical analysis using finite-difference time-domain method shows that the coupling length of this scheme is 510nm for the input power of 100W/µm and at the wavelength of 1310nm. Also, the proposed coupler exhibits the coupling efficiency of more than 80% for the input power of 100W/µm over all the optical communication bands (1260-1675nm). The other property of this coupler is being able to control the output power which is the advantage of using the nonlinear waveguides.

In this paper, based on the rate equations model for injection locked semiconductor ring laser, new equations are developed for modelling the detuning frequency and direct modulation response of the slave laser as well as the amplitude and phase modulation response of the master laser. Results show that by increasing the injection power of the master laser, the dynamic characteristics of the slave laser are improved consequently, locking area range, relaxation oscillation frequency and slave laser direct modulation bandwidth as well as field amplitude and phase of the master laser are enhanced by comparing the result of different modulation schemes. It is observed that master phase modulation configuration provides the largest bandwidth while the output response is considered amplitude of the slave laser (80 times of the slave laser direct modulation frequency response).

One of the most widely used current mode circuits is the current conveyor circuit, so that after current mirrors, they are the most commonly used circuits in the analog field. The ability to process voltage and current at the same time, more bandwidth, gain more bandwidth and gain independence form bandwidth, are notable features of this circuit. In this article, we have designed a logarithm circuit with flow conveyors based on flow theory, so that while eliminating the defects of previous circuits, we can also have innovations in the design of a logarithm circuits. Presenting a new method for designing logarithmic circuit based on current mode, using current conveyors to design logarithmic circuit, using low power and voltage circuits, circuit design with temperature independent output and thus more stability are the results of this article. In this new method, relying on the mathematical relations that lead to the natural input logarithm, a new logarithm circuit is designed. Design of the new logarithm circuit is done in 180 microwatt technology, in the current mode. The current consumption of the circuit is about 5.96 µA, the power consumption is 2.98 µW, the gain is 69.7 dB and bandwidth is about 85 kHz. The correct operation of the designed circuit has been investigated by simulation in HSPICE and MATLAB software.

In this paper, we present a 5T GC-eDRAM cell in FinFET technology. The memory structure is designed utilizing both p and n-type transistors to eliminate the clock feedthrough, multiple threshold voltages, and stack effect, thus lowering static power consumption. In the data path, a series of low power transistors are used to minimize the leakage current due to the stack effect. This allows achieving higher retention time and low static power consumption. The improved data retention time and reduced refresh frequency, refresh power, and retention power will be achieved, due to the slower failure of data 1 and 0. Our design has a new structure, high data retention time, as well as a low static and retention power among GC-eDRAMs with similar structures. The cell is, therefore, simultaneously classified as ultra-low-power and high-speed. Simulations of the proposed cell were performed at all dimensions less than 22 nm using the Hspice software. Compared to a 4T cell in 28 nm FD-SOI technology, the proposed cell in 20 nm FinFET has 195 times higher DRT, 80% lower static power consumption, and 48% smaller cell area.

Cognitive radio is well known as an ultra-wideband communication system that intelligently optimizes the available frequency spectrum by implementing the dynamic spectrum monitoring method. The receiver of such a system requires an ultra-wideband low noise amplifier (from 50 MHz to 10 GHz). In this work, a low power ultra-wideband low noise amplifier is proposed by quasi-differential structure and active inductors. Using active circuits as inductors, in addition to expanding the amplifier bandwidth and reducing the chip area, have an inherent gain and due to their high quality factor, have the ability to adjust the inductance and frequency. In addition, the quasi-differential structure increases the transconductance of transistors and reduces both power consumption and noise figure of this circuit. The simulation results in a 0.18 µm CMOS process show that the proposed LNA achieved NF changes 3-7 dB and input matching less than -10 dB in the 0.05-10 GHz band. The voltage gain changes from 16.5-19 dB and third-order intercept point is -6.7 dBm. The power consumption of the main circuit is 1.98 mW with 1.8 V power supply.

Networks-on-Chip (NoC) is an effective solution to inside on-chip communication problems. In recent years, the advancement of silicon industry and also the production of Carbone Nanotube antennas (CNTs), it is possible to use wireless antennas in the network on chip. This new communication structure is called a Wireless Network on Chip (WiNoC). Using the wireless antenna in multi-core chips led to the reduction in the latency and power consumption. In spite of higher bandwidth of the WiNoCs compared to wire NoCs; their performance can decrease significantly in absence of an effective flow control mechanism.In this paper, the flow control problem is investigated by using a mathematical model in the WiNoC. The aim of this study is showing a correct analysis of cores behaviors with the subject to capacity constraints of links and flow rates. The simulation results of traffic pattern in the WiNoC show that the proposed flow rate control algorithm has been able to control and adjust the flow injection rate of each transmitter with acceptable convergence

Proton exchange membrane fuel cells (PEMFC) have gained more attention as promising successors to fossil fuels due to their high efficiency and power density, low operating temperature, and no need for maintenance. In this research, first, the cell performance is investigated based on the step changes of current density using the Nernst equation and the governing equations of the fuel cell. Then, an equivalent electrical circuit model considering the effects of current density, temperature, and pressure on the fuel cell performance is presented. MATLAB software is utilized to solve governing equations of the cell. Comparison of the model results with experimental data shows that the model’s performance at low current densities is very well in line with the experimental results (with an error of less than 3.62%). Also, the proposed model shows an acceptable estimate (with an error of about 17.66%) of the cell performance in the whole range of simulated current densities (0 to 0.6 A/cm2). Finally, the effect of operating temperature and anode and cathode pressures on the fuel cell’s performance is investigated and shown that the cell voltage increases by increasing these parameters which, implies their constructive effect on the cell performance.

Modeling and simulation play a critical role in designing, developing, and predicting the performance of photovoltaic systems. This paper aims to develop a new approach to simulate a solar cell's electrical and thermal performance under various environmental conditions. Heat and temperature modeling in a commercial silicon solar cell has been performed by calculating heat losses via heat transfer method. The effects of environmental conditions on the temperature distribution and electrical efficiency of the solar cell, such as variations in the intensity of sunlight using a concentrator, ambient temperature, and wind speed, have also been investigated. Increasing the intensity of sunlight by using a concentrator will increase the output power of the solar cell. The simulation results show that under standard test conditions (STC), the output power of the modeled solar cell without a concentrator is 14.3 mW/cm2, whereas the output power of the same cell in the presence of a concentrator device with a concentration ratio of two is 25.7 mW/cm2. On the other hand, as temperature rises, the efficiency and output power of the solar cell decreases by 0.4-0.5 %/oC in both concentrator and non-concentrator modes. Also, the obtained results indicate that as wind speed rises, the temperature difference between the solar cell and the ambient decreases.

In this paper, Ensemble Monte Carlo modeling and simulation of two-junction solar cells is presented. Nowadays, due to the importance of designing the high efficiency solar cells, using of two-junction solar cells to construct solar panels has become inevitable. Although, this approach will increase the complexity of the cell structure, but analyzing the electrical and electronic device behavior prior to initiating the manufacturing process will speed up the achieving of optimal cell. Considering the random motion of electrons and holes in the solar cell and the capability of the Monte Carlo algorithm to model random phenomena, it provides more accurate information about device than drift-diffusion and hydrodynamic methods. In this study, in order to further adapt the behavior of the modeled device to its fabricated, the Monte Carlo method was used to simulate the drift-scat of particles. In addition, in order to solve the Poisson equation and to calculate the potential distribution and electric field, the Gamel numerical method is used. Electrical and electronic features derived are included as the distribution of intrinsic and photo carrier concentrations, the energy distribution of the carrier along to the cell, the electrical potential distribution and the cell current-voltage curve. In this research MATLAB R2018b (9.5.0) software had been used to simulate of two-junction solar cell based on Ensemble Monte Carlo Algorithm.

In this paper, a new Countinous Current Mode (CCM) nonisolated DC-DC interleaved converter is proposed that uses an auxiliary circuit to create soft switching conditions. The auxiliary circuit used in this converter has no switch and provides soft switching conditions for all switching elements from only one resonance path. Also, the designed auxiliary circuit is not located in the power transmission path from input to output. Due to the use of interleaved structure, the current stress on the switches in the proposed converter is low. This converter has a very simple structure and operation compared to similar structures. The converter control method is also simple and pulse width modulation (PWM) is used to command the switch. Due to soft switching technique, the switching frequency of the converter has increased, this way leads reduced the size and overall volume of the converter, especially the passive elements in the structure, switching and conduction losses and also increased the efficiency of the converter compared to similar structures. Also, the proposed converter with 48V input voltage, 155V output voltage with 100 kHz switching frequency at 900 watts was simulated in PSPICE software. The results of simulation and theoretical and computational analysis confirm the performance of the converter.

Image rectification is an essential part in most multi-view systems. It simplifies the stereo matching process by limiting the search path to horizontal and parallel lines. The main problem in conventional image rectification is complexity and mass computation load for calculation of calibration matrix, projection matrix and fundamental matrix. In this paper an efficient and confident method is presented to rectify stereo images based on the correspondence field (CF) that avoids the mentioned matrices. In general this algorithm is able to generate an image for any new camera position. Especially one can choose the new position in such a way that the virtual camera generates a rectified image. Both real and simulated stereo images are used to evaluate the proposed algorithm. SIFT algorithm is used to estimate how close is the generated image to original rectified one. Simulation results show the mean absolute error and standard deviation are about 0.25 and 0.26 pixels respectively and rectified image is independent of focal ratio.

In imaging a scene, it is possible that the depth of the objects in the scene is larger than the depth of the field of the lens. In such cases, just objects or parts of objects that are in depth of field of the lens, will be sharp in the image while others become blurred. To overcome this problem, a technique called image fusion is used. In this technique, by changing the focus of the camera, some images are acquired such a way that in each image, part of the scene is sharp. Afterwards, these images (called multi focus images) are fused in a single image such that all parts be sharp. In this paper, a multi-focus image fusion method is proposed. The proposed method is based on an existing method which uses comparison of spectrum of the Fourier transform. In the existing method, first, salient regions are detected in each input image. Then, by comparing salient regions of input images, a focus map is extracted. In mentioned method, the amplitude of Fourier transform is processed while no process is performed on the phase angle. In this paper, a method taken from another paper is employed to benefit the phase information of the Fourier transform. In the proposed method, firstly, three images (called feature maps) are created by combining color channels of each input image. Afterwards, phase information is extracted from feature maps in frequency domain. Using them, salient parts of the images are determined and finally the fused image is produced. By testing the proposed method on some images, the results improved in comparison with the base method and some other methods.

Classification of brain tumors using MRI images along with medical knowledge can lead to proper decision-making on the patient's condition. Also, classification of benign or malignant tumors is one of the challenging issues due to the need for detailed analysis of tumor tissue. Therefore, addressing this field using image processing techniques can be very important. In this paper, various types of texture-based and statistical-based features are used to determine the type of brain tumor and different types of features are applied in this classification procedure. Sparse coding and dictionary learning techniques are used to learn the over-complete models based on the characteristics of each data category. The classification process is carried out based on the calculated energy of the sparse coefficients. Also, the results of this categorization are compared with the results of the classification based on the neural network and support vector machine. The simulation results show that the proposed method based on the selected combinational features and learning the over-complete dictionaries can be able to classify the types of brain tumors precisely.

Due to the wide range of Persian (Farsi) machine-printed sub-words, finding a sub-word and consequently a word in a machine-printed text will be very time consuming. In this paper, a biometric minutia based method is presented, which significantly reduces the search space of Farsi sub-words. Therefore, the number of points and their coordinates of bifurcated and ending minutia points, which are two well-known features in the field of biometrics, have been used as features to reduce the search space in the form of a two-step method. In the first step, the minutia points are extracted from the sub-word image and categorized into four clusters that are close and similar as a viewpoint of the number of minutia points. Therefore, search space will be halved by this step. In the second step, by creating a repository of the distances between the first and the last end points for each sub-word in each cluster and matching the same distance of experimental image with the repository, the search space is significantly reduced. Obtained results show that the search space has been reduced from 12,700 sub-words to about 500 sub-keywords with an accuracy of approximately 90%.

In recent years, machine learning approaches play an important role in quality identification of  manufactured products including tiles and ceramics. Deviation of tile dimensions is one the main challenge in ceramic and tile industry. Prediction of this deformation will be beneficial if it can be predicted before producing the tile. In this paper, an automatic system has been proposed to predict the deviation of the ceramic tiles. Besides, a machine learning approach is utilized to identify the most effective parameters that leads to tiles’ defect. In this way, three different classification approaches including logistic regression, random forest, and support vector machine have been studied and the best solution is determined for this purpose. Moreover, several feature sets and forward feature selection method have been employed to select more effective variables on our decision making. The experimental results conducted on real-world dataset show that, random forest approach achieves better performance than others, and the results illustrate that improper temperature parameters has more effect on tile deviation.

This paper presents reinforcement learning-based learning methods for designing a supervisor for automatic driving in the highway environment. Due to the random driving conditions on the highway as well as the more realistic driving conditions, the benefits of deep distributed reinforcement learning have been exploited. In this paper, for the first time, the use of Fully Parameterized Quantile Function (FQF) and Implicit Quantile Network (IQN) distributed learning methods is proposed to learn driving policies. To train the agent using the camera data, the LIDAR sensor and its combination are suggested. In order to use the combination of the two types of data, we have employed a multi-input network structure. To evaluate the proposed methods, we have used the highway driving simulator developed in unity software. The realization of the car in the simulator is done with the help of driver assistance systems. Agent evaluation is based on a learning driving policy that can choose the right action to steer the car. In order to better evaluate the methods, we have examined the two criteria of speed changes and lane changes for learning driving policy. The results obtained from the article were compared with methods such as DQN, ‌QR-DQN that were previously presented. The results show that the proposed algorithms can learn appropriate driving policies in the highway environment. The FQF method also performs better than IQN and other strategies implemented in the past.

In this paper a modified adaptive robust composite control scheme for fully-constrained cable-driven robots with elastic cables is presented here. At first, an adaptive control algorithm based on sliding mode control method is introduced for rigid robots using internal force concept to ensure that all cables are remained in tension through the whole workspace. Then, the dynamic equations are turned into singularly perturbed form to develop the adaptive controller of rigid robots to be used for robots with elastic cables. To improve the stability and robust performance of the system as an achievement of the paper, a correction term is added to the adaption law of rigid robots and the stability analysis of the system is conducted using the Lyapunov theory. Finally, while the simulation results are given for intended robot with elastic cables, performance of the proposed algorithm against the uncertainty of dynamic parameters is proved and by comparing the performance of the proposed control scheme in this paper with those related to the initial robust adaptive controller a remarkable improvement in the accuracy of the desired path tracking confirmed.

In this paper, a delay compensation for a class of unknown systems with simultaneous state and input delays through the predictive adaptive observer is investigated. Despite the unknown term, an adaptive state observer is designed to have an exact prediction. The upper bounds of uncertainty with time varying delay are considered unknown, which do not require to be known for the design. Then, the second observer is introduced for simultaneous delay compensation in state and input that input delay can be optionally longer than the delay in the state. Besides, in this approach, the numerical solution is not implied for delay compensation of input in uncertainty system. Based on Lyapunov theory, it has been proved, the input delay in systems with the unknown term can compensate by means of designed observers, while the estimation error is small and bounded. For more details, we also show a numerical example that indicates the proposed approaches’ impact.