A wireless microwave monitoring system for measuring the pollutants of triclosan and glyphosate in water is proposed in this manuscript. The proposed wireless microwave monitoring system is a long-range, low-power internet of thighs (IoT) system, which can serve as a sensing node for monitoring water quality in a large-scale water area. The microwave sensing system and the LoRa-based wireless communication module constitute the real-time online wireless microwave monitoring network. The microwave sensing system is composed of the multiple stepped impedance transformer (MSIT) with an open-ended stub, radio frequency (RF) oscillator, and frequency demodulation circuit. To suppress the external interference, the microwave sensing system is designed as the differential configuration. The test channel of microwave sensing system outputs the pollutant concentration-dependent DC voltage, transmits this DC voltage to a laptop terminal via LoRa transceiver module, and the host computer system on the laptop terminal can display the concentrations of triclosan and glyphosate in distilled water by the trained BP-NN model. In measurement, a sensitivity of 1.33553mV/(μg/mL) and a limit of detection (LOD) of 1.001 μg/mLare calculated for water-triclosan mixture. In contrast, a sensitivity of 0.87638mV/(μg/mL) and a LOD of 1.525 μg/mLare obtained for water-glyphosate mixture. The measured results demonstrate that the proposed wireless microwave monitoring system is an attractive role for industrial application.

With the increasing demand for higher bandwidth and frequency in high-speed digital systems, the interference of common-mode (CM) noise in differential signal transmission has become more severe. Common-mode suppression filters (CMF) were proposed to solve this issue, but their design process typically relies on empirical parameter tuning with extensive electromagnetic simulations, which not only increases design costs but also limits efficiency and flexibility. In this paper, a transformer-based inverse design method for CMFs is proposed for the first time, and it can eliminate the need for empirical parameter adjustments by automatically predicting the targeted geometric parameters, thereby improving the design efficiency. In addition, to address the problem of imbalanced data distribution, the multilabel synthetic minority over-sampling technique (MLSMOTE), which can enhance the data representation in sparse sample regions, is implemented. Further validation on tunable CMFs confirms that the proposed inverse design method has broad applicability. The experimental results demonstrate that the proposed inverse design method can accurately predict the geometric parameters and improve the efficiency, thereby providing an innovative solution for the design and applications of CMFs.

In this work, an efficient thermal-aware placement optimization framework is developed by combining a fast thermal simulation method with intelligent optimization algorithm. The fast thermal simulation method for 2.5-D advanced packaging is proposed by improving global stiffness matrix and load vector reconstruction process of traditional finite element method. Its performance is verified by comparing it with the traditional finite element method, the fast steady-state solver of the open-source HotSpot, and commercial software in terms of computational accuracy and efficiency. Then, an improved genetic algorithm, the elite immigrant primal-dual genetic algorithm is developed for effective placement optimization. By combining the fast thermal simulation method with the elite immigrant primal-dual genetic algorithm, the efficient thermal-aware placement framework for 2.5-D advanced packaging is established. The performance of the proposed optimization framework is then evaluated through several cases studies, including comparisons with optimization frameworks with other intelligent algorithms in terms of effective layout area, maximum temperature, temperature uniformity, and total wirelength. Additionally, its time efficiency is compared with frameworks based on other thermal simulation methods.

This paper proposes a novel neural network architecture combining convolutional and transposed convolutional neural networks to accurately and efficiently modelS-parameter of interconnects for 3D integration. The network incorporates physical consistency constraints, specifically causality and passivity, into its design to ensure the physical effectiveness of the output. The transposed convolutional network serves as a sub-network to map the relationship between the geometrical parameters andS-parameter for sub-structures. Then, theS-parameters of individual sub-structures are cascaded for dealing with a complex structure composed of sub-structures. A coupling neural network, with causality and passivity constraints, is developed to map the coarse cascadedS-parameters to the fine accurateS-parameters. With the help of this high-dimensional space mapping, a small amount of electromagnetic simulation data of complex interconnect structures is sufficient to learn the relationship between cascaded and realS-parameters. To ensure the completeness of the training set distribution when training CONN on small datasets, a sensitivity analysis-based training set screening method is proposed to enhance the training performance of CONN. The proposed algorithm is demonstrated in two different assemble structure applications. The results highlight the effectiveness, flexibility and versatility of the proposed architecture in modeling complex structures with small costly simulation data while maintaining accuracy and physical consistency.

In this work, a finite element method (FEM)-based numerical framework is proposed to effectively calculate the drift-diffusion equations and compiled into a parallel computing device simulator. In this framework, a novel upwind FEM is developed to solve the convection dominated continuity equations. In the implementation of the upwind method, the vector basis functions are employed to interpolate the edge streamline upwind (SU) current densities into mesh grid to obtain the spatial current density, and then the scalar FEM is used to construct the element matrix equation. Through comparing the calculating results of a 2-D PN-junction with those obtained by the COMSOL Semiconductor, the accuracy of proposed framework is verified first. Then, through several numerical cases, its advantages in comparison with FBSG-, SU Petrov Galerkin (SUPG)-, or control-volume-finite-element method SUPG-based frameworks in terms of mesh grid adaptivity, computing stability, and efficiency are presented. At last, by combining the proposed framework with a domain decomposition scheme and a fully coupled Newton’s approach, a parallel computing device simulator is developed, including both steady-state and transient solvers. The performance of the in-house simulator is evaluated in terms of calculating accuracy, large-scale problem solution capability, and scalabilities.

Analog circuit topology synthesis suffers from weak synthesis capability and low-synthesis efficiency, which result in a bottleneck toward its practical industrial applications. This article presents a proximal-policy-optimization-based circuit topology synthesis framework, which features a superior convergence rate. To further promote its synthesis efficiency, we have improved a deterministic optimization method by incorporating a bias-aware scheme and group concept, which is applied as a filter to eliminate the undesirable topologies in the early evaluation stage. Moreover, a graph-based refinement scheme is proposed to perform deterministically on the generated circuit topologies, which can efficiently add frequency compensation circuits. Compared with the state-of-the-art approaches, our proposed method not only boosts the synthesis efficiency by at least 3 times but also enhances the synthesis capability with a deterministic compensation scheme, showcasing significant advancement of performance efficacy.

As an important technology in high-speed systems, equalizer (EQ) is used to mitigate inter-symbol interference (ISI) caused by inconsistent attenuation of high and low frequencies. The difficulty of signal integrity improvement increases the complexity of EQ design, making the existing algorithms inefficient in high-dimensional searching and constraint processing. In this article, a local multi-constraint modeling-Bayesian optimization (BO) with region partitioning is proposed, aiming to provide a general optimization solution for high-dimensional multi-constraint EQs and improve convergence accuracy and efficiency. The constraint filtering mechanism is used to exclude areas that violate simulation-independent constraints. Local modeling and region partitioning techniques complement each other, taking into account both the local accuracy of the model and the global search performance of the algorithm. The multi-constraint modeling strategy allows simulation-dependent constraints to be pre-judged through the surrogate model, overcoming the shortcomings of the traditional solution of adding the penalty term to the target value, which makes it difficult to balance the weights and can only judge the constraints after simulation, thereby reducing the waste of computing resources caused by simulating data that violates the constraints. The proposed algorithm is applied to EQ optimization in a 16 Gbps high-bandwidth memory channel and a 64 Gbps differential peripheral component interconnect express channel, respectively. The algorithm is developed based on PyTorch, and the eye diagrams are obtained using Keysight ADS software. Two applications are conducted on computer with Intel Core i5-13500 processor and 32 GB RAM. By utilizing the region partitioning and constraint filtering techniques, the actual number of simulations in the optimization can be significantly reduced. The experimental results demonstrate that the proposed algorithm has significant shorter computing time than traditional BO and genetic algorithm, implying its practical application potential for dealing with high-dimensional multi-constraint problems.

Thermal issues and power integrity problems have long been recognized as critical challenges in the design of 2.5-D integrated microsystems. In this paper, a thermal and power integrity co-optimization framework, consisting of a sequential two-stage design and driven by the improved adaptive genetic algorithm, is proposed. In the first stage, the thermally-aware placement, comprehending key design indexes encompassing area, peak temperature, temperature uniformity, and wire routing constrained by multicommodity flow constraints of multi-dies, is addressed. In the second stage, the transmission matrix method is first utilized to characterize the impedance of the hierarchic power delivery network based on the determined chip architecture from the first stage. Then, the initial impedance of the observed port, directly correlated with simultaneous switching noise, is reduced by the layout optimization of on-chip decoupling capacitances. Following this, a benchmark case is established to validate the design framework and its embedded algorithm from multiple perspectives. The results demonstrate that the proposed framework is capable of generating superior solutions compared to mainstream algorithms. Additionally, the proposed design methodology is compared with that of the state-of-the-art studies, revealing that this work bridges the thermal and power integrity domains. This accomplishment provides a novel design paradigm of electronic design automation tools targeting large-scale and highly complex 2.5-D integrated microsystems in the future.

A 3-D domain decomposition method (DDM) with nonconforming meshes, based on the discontinuous Galerkin spectral element time-domain (DG-SETD) algorithm, is first extended for thermoelastic large-scale and multiscale modeling. This method is based on second-order elastic wave equation and considers various temperature-dependent material properties under multiscale temperature distributions. Additionally, a hybrid explicit-implicit time-stepping scheme is incorporated into the DDM to facilitate large-scale and multiscale efficient computations. For subdomains with fine structures or large thermal gradients and therefore fine meshes, an implicit scheme is adopted to obtain unconditionally stable time step increment. For subdomains with coarse meshes, an explicit scheme is employed and the time step increment is limited by the Courant−Friedrichs−Lewy (CFL) stability condition. Furthermore, a thin layer equivalent (TLE) method for fracture modeling is introduced to enhance the multiscale computational efficiency of our algorithm. The numerical results show that, compared with commercial software COMSOL, our algorithm achieves an accuracy exceeding 95% and an efficiency surpassing three times, demonstrating the higher performance of the DG-SETD algorithm.

Memristors have been widely studied as artificial synapses in neuromorphic circuits, due to their functional similarity with biological synapses, low operating power, and high integration density. Currently, the synaptic weight symbolic limitation and weight update inaccuracy are two challenging issues to be solved. In this work, a novel memristive synapse and a matched mixed-signal neuron circuit are designed to implement robust yet accurate spike-timing-dependent plasticity learning in excitatory and inhibitory synapses. To break through the weight symbolic limitation, a four memristors and two resistors (4M2R) synapse composed of 4M2R for spiking neural network (SNN) is designed. The proposed synapse can be either excitatory or inhibitory (E/I) by rationally arranging the resistors in the circuit, and it is the first of its kind, enabling Hebbian and anti-Hebbian training without additional adjusting of neural signals. In addition, the high symmetricity, linearity, and stability against device variation of the 4M2R synapse can also greatly improve the weight update accuracy. To further address the inaccurate weight update issue caused by signal complexity, a neuron circuit is designed to generate square-wave pulses for spike transmission and synaptic weight modulation. Simulations are carried out in the MATLAB Simscape as well as Virtuoso using SMIC 0.18$\mu $m process and a specially developed memristor model for SNN synapse simulation. The simulating results show good agreement with the weight change derived from the algorithmic methods, and the influence of weak signal-induced weight variation on circuit performance can be rigorously assessed.

The performance of gas sensors based on micro-heater is highly dependent on the environment temperature, heater temperature, and gas flow rate. The performance fluctuation of gas sensors induced by environment temperature drift can be reduced in different ways. In this article, different sizes and shapes of obstacles are placed around the sensitive component of the thermal conductivity gas sensor to study the effect of gas flow rate on the gas sensor performance. Both the simulation and experiment results indicate that the obstacle design can reduce the interference of gas flow rate on gas sensor performance. As the obstacle size increases, the influence of gas flow rate decreases and the dynamic detection accuracy of the gas sensor increases at the same gas flow rate, and the voltage variation of the sensor with quadrangular prism obstacle shape is minimal, and the gas velocity reduction effect is 1.77% better than cylinder obstacle shape. The effect of gas flow rate on the gas response is also investigated by detecting the voltage response of the MG811 gas sensor with and without thermostatical control. The result shows that the gas flow rate not only reduces the temperature of the sensor (direct influence), but also promotes the chemical reaction of the sensor, which releases a large amount of heat (indirect influence). The corresponding relationship between voltage response and gas flow rate is obtained through controlling the temperature of sensitive components thermostatically. This article provides a new angle for improving the accuracy of the gas sensor in different gas flow rate environments, which can improve the detection rate of the wireless monitoring system.

Industrial gas leaks cause accidents and pose threats to the environment and human life. Thus, it is essential to detect gas leaks in time. Usually, the abnormal concentration signals are defined by a fixed concentration value, such as 25% of the lower explosive limit. However, it is difficult to accumulate to the fixed point quickly when the leak is small. In addition, the actual leak signals are seldom available, making many data classifications inoperable. To solve these problems, this paper proposes a detection approach using the auto-correlation function (ACF) of the normal concentration segment. The feature of each normal segment is obtained by calculating the correlation coefficients between ACFs. According to the features of statistical analysis, a nonconcentration threshold is determined to detect the real-time signals. In addition, the weighted fusion algorithm based on the distance between the sensors and virtual leak source is used to fuse multisensory data. The proposed method has been implemented in a field by building a wireless sensor network. It is confirmed that the system detection rate reaches as high as 96.7% and the average detection time delay is less than 30 s on the premise of low false alarm rate.

A compact frequency selective surface (FSS) for 5G applications has been designed based on 2.5‐dimensional Jerusalem cross. The proposed element consists of two main parts: the successive segments of the metal traces placed alternately on the two surfaces of the substrate and the vertical vias connecting traces. Compared with previous published two‐dimensional miniaturized elements, the transmission curves indicate a significant size reduction (1/26 wavelengths at the resonant frequency) and exhibit good angular and polarization stabilities. Furthermore, a general equivalent circuit model is established to provide direct physical insight into the operating principle of this FSS. A prototype of the proposed FSS has been fabricated and measured, and the results validate this design.