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This paper examines multiscale theories and numerical methods for interconnect materials in electronic packaging, focusing on the interplay among micro-scale morphology, meso-scale structure, and macro-scale behavior to improve material reliability and performance prediction. It reviews advanced materials, such as sintered silver and lead-free solder, alongside methodologies like Molecular Dynamics (MD) simulations, cohesive modeling, crystal plasticity modeling, and phase-field modeling, to evaluate mechanical and thermal properties across scales and their long-term reliability. At the microscopic scale, MD simulations reveal the influence of atomic arrangements, grain orientations, and dislocation evolution on mechanical behavior. At the mesoscopic scale, phase-field and crystal plasticity models are combined to analyze pore evolution, grain sliding, and stress concentration under thermal cycling. Macroscopically, models like Anand and Unified Creep Plasticity (UCP) describe viscoplasticity, creep, and fatigue life, offering insights into performance under complex conditions. By systematically integrating diverse research methods and theoretical models, this review highlights the applicability of a multiscale coupling framework, providing a comprehensive understanding of the correlations between morphology, structure, and behavior. This framework serves as theoretical guidance for developing innovative packaging solutions and optimizing materials for high-density, low-power electronic devices. [ABSTRACT FROM AUTHOR]

Sn–Bi-based, low-temperature solder alloys are being developed to offer the electronics manufacturing industry a path to lower temperature processes. A critical challenge is the significant microstructural and lattice parameter changes that these alloys undergo at typical service temperatures, largely due to the variable solubility of Bi during the Sn phase. The influence of alloying additions in improving the performance of these alloys is the subject of much research. This study aims to enhance the understanding of how alloying with In influences these properties, which are crucial for improving the alloy's reliability. Using in situ heating synchrotron powder X-ray diffraction (PXRD), we investigated the Sn–57 wt% Bi–xIn (x = 0, 0.2, 0.5, 1, 3 wt%) alloys during heating and cooling. Our findings reveal that In modifies the microstructure, promoting more homogeneous Bi distribution during thermal cycling. This study not only provides new insights into the dissolution and precipitation behaviour of Bi in Sn–Bi-based alloys, but also demonstrates the potential of In to improve the thermal stability of these alloys. These innovations contribute significantly to advancing the performance and reliability of Sn–Bi-based, low-temperature solder alloys. [ABSTRACT FROM AUTHOR]

Microelectronics packages play a vital role in not only interconnecting the electronic signals from the die to the printed circuit board (PCB), but also in protecting the chips during the manufacturing process and their subsequent service lives. Epoxy molding compound (EMC) is widely used in electronic packaging due to its superior processing capability and low circuit signal delay. However, interfacial delamination is a common problem in encapsulated silicon devices, particularly at the interface between the copper leadframe (LF) pads and the EMC due to the weaker adhesion strength. Accordingly, the present study employs a double cantilever beam (DCB) experimental testing method and a numerical model based on the virtual crack closure technique (VCCT) to investigate the fracture behavior at the EMC/Cu LF interface in a quad flat no leads (QFN) package. The experiments are performed on an MTS-Acumen microforce tester equipped with a load unit capable of applying a force of 0.01 to 1250 N with a displacement resolution of 0.1 μm. The DCB specimens are prepared with a pre-crack length of 12 mm. The validity of the simulation model is confirmed by comparing the predicted values of the critical strain energy release rate (SERR, Gc) between the EMC and the copper LF pads with the experimental observations. In general, the results show that the Gc value provides a useful parameter for evaluating the delamination risk of encapsulated microelectronics packages and assessing the reliability of alternative package architectures. [ABSTRACT FROM AUTHOR]

This paper examines multiscale theories and numerical methods for interconnect materials in electronic packaging, focusing on the interplay among micro-scale morphology, meso-scale structure, and macro-scale behavior to improve material reliability and performance prediction. It reviews advanced materials, such as sintered silver and lead-free solder, alongside methodologies like Molecular Dynamics (MD) simulations, cohesive modeling, crystal plasticity modeling, and phase-field modeling, to evaluate mechanical and thermal properties across scales and their long-term reliability. At the microscopic scale, MD simulations reveal the influence of atomic arrangements, grain orientations, and dislocation evolution on mechanical behavior. At the mesoscopic scale, phase-field and crystal plasticity models are combined to analyze pore evolution, grain sliding, and stress concentration under thermal cycling. Macroscopically, models like Anand and Unified Creep Plasticity (UCP) describe viscoplasticity, creep, and fatigue life, offering insights into performance under complex conditions. By systematically integrating diverse research methods and theoretical models, this review highlights the applicability of a multiscale coupling framework, providing a comprehensive understanding of the correlations between morphology, structure, and behavior. This framework serves as theoretical guidance for developing innovative packaging solutions and optimizing materials for high-density, low-power electronic devices.

Image reconstruction of low-count positron emission tomography (PET) data is challenging. Kernel methods address the challenge by incorporating image prior information in the forward model of iterative PET image reconstruction. The kernelized expectation-maximization (KEM) algorithm has been developed and demonstrated to be effective and easy to implement. A common approach for a further improvement of the kernel method would be adding an explicit regularization, which however leads to a complex optimization problem. In this paper, we propose an implicit regularization for the kernel method by using a deep coefficient prior, which represents the kernel coefficient image in the PET forward model using a convolutional neural-network. To solve the maximum-likelihood neural network-based reconstruction problem, we apply the principle of optimization transfer to derive a neural KEM algorithm. Each iteration of the algorithm consists of two separate steps: a KEM step for image update from the projection data and a deep-learning step in the image domain for updating the kernel coefficient image using the neural network. This optimization algorithm is guaranteed to monotonically increase the data likelihood. The results from computer simulations and real patient data have demonstrated that the neural KEM can outperform existing KEM and deep image prior methods.

The potential of Sn-Bi alloys as low-temperature solders for electronics manufacturing has spurred significant research on their mechanical properties, both in the as-soldered condition and after aging. Previous studies have demonstrated that, because of the extreme temperature sensitivity of the solubility of Bi in Sn, the mechanical properties of Sn-Bi solder alloys are very sensitive to their thermal history. While the properties of the bulk solder alloy are a factor in its performance as a solder joint, the reliability in service is also affected by the joint geometry and the interaction of the solder alloy with the joint substrate. In the work reported in this paper the effect of thermal history on solder joints formed with representative Sn-Bi alloy solder balls was assessed by measuring the performance in a standard ball shear test of a solder ball reflowed to solder mask defined (SMD) copper pads with organic solderability preservative (OSP) or electroless nickel/immersion gold (ENIG) finishes. The solder ball/substrate combinations were tested within 10 min of reflow and after room-temperature storage for up to 10 days to determine the effect of aging on their response to the ball shear test. Our results show that the peak force and fracture mode of Sn-Bi solder joints is influenced by the Sn-Bi alloy composition, the substrate type, and the aging time. These observations provide new information on the capability of these alloys to deliver reliable service over a range of operating conditions. [ABSTRACT FROM AUTHOR]

With low liquidus temperatures, low raw material costs, and non-toxicity, Sn-Bi low-temperature solders are promising candidates for the replacement of the currently widely-used lead-free solders in situations in which process temperatures have to be reduced. Electrical resistivity is one of the most important properties of solder alloys, as one of their primary functions is to conduct electrons between the connected components. The electrical resistivity of an alloy of a given composition at a specific temperature and pressure is affected by the microstructure and the crystal structure of the phases present. For Sn-Bi solders, the solubility of Bi in Sn is highly temperature-sensitive and increases from 3 wt.% at room temperature to 21 wt.% at 139°C, the eutectic temperature of the Sn-Bi system. As the temperature increases within that interval, Bi will dissolve in Sn, while it will precipitate as the temperature decreases. The resulting significant changes in the overall microstructure and the lattice parameters of the Sn phase can be expected to affect the electrical resistivity. In this study, the electrical resistivity of hypo-eutectic Sn-37wt.%Bi and near-eutectic Sn-57wt.%Bi alloys was measured as a function of temperature and the temperature coefficient of resistance (TCR) calculated. It was found that the electrical resistivity increases linearly with increasing temperature up to 70°C, while above 80°C, a deviation from the linear relationship was observed. This deviation is attributed to the rapid dissolution of Bi in Sn at 80°C and above. [ABSTRACT FROM AUTHOR]

Thermal Interface Materials (TIMs) are widely used in electronic packaging. Increasing power density and limited assembly space pose high demands on thermal management. Large cooling surfaces need to be covered efficiently. When joining the heatsink, previously dispensed TIM spreads over the cooling surface. Recommendations on the dispense pattern exist only for simple surface geometries such as rectangles. For more complex geometries, Computational Fluid Dynamics (CFD) simulations are used in combination with manual experiments. While CFD simulations offer a high accuracy, they involve simulation experts and are rather expensive to set up. We propose a lightweight heuristic to model the spreading behavior of TIM. We further speed up the calculation by training an Artificial Neural Network (ANN) on data from this model. This offers rapid computation times and further supplies gradient information. This ANN can not only be used to aid manual pattern design of TIM, but also enables an automated pattern optimization. We compare this approach against the state-of-the-art and use real product samples for validation.

There is a growing interest in the development of microelectronics that can perform reliably and robustly at temperatures above 300 °C. Such devices require stable thermal properties, low thermal drift, and thermal cycling resistance. Conventional hybrid circuit technology demonstrates high‐temperature packages, but the high costs and lead time are significant drawbacks. In contrast, additive manufacturing processes, including aerosol jet printing (AJP), offer cost and time benefits, as well as 3D structures and embedded features. However, the properties and reliability of additive packaging materials at extreme temperatures are not well known. Herein, the reliability at temperatures up to 750 °C in terms of electrical performance and mechanical strength of aerosol jet printed gold thick films onto ceramic substrates are assessed. Thermal coefficient of resistance of printed gold films is measured. The electrical resistance stability and leakage current of printed gold structures are also characterized during over 100 h of aging at temperatures up to 750 °C. Finally, the mechanical adhesion strength of the printed gold films is evaluated after aging for 100 h at temperatures up to 750 °C. The adhesion of the printed gold to the ceramic substrates remains high after aging, very stable resistances and minimal leakage currents have been observed. [ABSTRACT FROM AUTHOR]

In the design of prosthetic hand fingers, achieving human-like movement while meeting anthropomorphic demands such as appearance, size, and lightweight is quite challenging. Human finger movement involves two distinct motion characters during natural reach-and-grasp tasks: consistency in the reaching stage and adaptability in the grasping stage. The former one enhances grasp stability and reduces control complexity; the latter one promotes the adaptability of finger to various objects. However, conventional tendon-driven prosthetic finger designs typically incorporate bulky actuation modules or complex tendon routes to reconcile the consistency and adaptability. In contrast, we propose a novel friction clutch consisting of a single tendon and slider, which is simple and compact enough to be configurated within the metacarpal bone. Through tactfully exploiting the friction force to balance the gravity effect on each phalanx during finger motion, this design effectively combines both consistency and adaptability. As a result, the prosthetic finger can maintain consistent motion unaffected by any spatial posture during reaching, execute adaptive motion during grasping, and automatically switch between them, resulting in human-like reach-and-grasp movements. Additionally, the proposed finger achieves a highly anthropomorphic design, weighing only 18.9 g and possessing the same size as an adult’s middle finger. Finally, a series of experiments validate the theoretical effectiveness and motion performance of the proposed design. Remarkably, the mechanical principle of the proposed friction clutch is beneficial to achieve highly anthropomorphic design, providing not only a new strategy to prosthetic hand design but also great potential in hand rehabilitation. [ABSTRACT FROM AUTHOR]

Multispectral remote-sensing images often have band-dependent image resolution due to cost and technical limitations. To address this, we developed a method that sharpens low-resolution (LR) images using high-resolution (HR) images. In this letter, we propose a novel unsupervised deep-learning (DL) approach that involves unrolling an iterative algorithm into a deep neural network and training it using a loss function based on Stein’s risk unbiased estimate (SURE) to sharpen the LR bands (20 and 60 m) of Sentinel-2 (S2) to their highest resolution (10 m). This approach views traditional optimization model-based methods through a DL framework, improving interpretability and clarifying connections between the two approaches. Results from both simulated and real S2 datasets demonstrate that the proposed method outperforms competitive methods and produces high-quality sharpened images for the 20- and 60-m bands. The codes are available at https://github.com/hvn2/S2-Unrolling. [ABSTRACT FROM AUTHOR]

In practical applications, airborne terahertz synthetic aperture radar (THz-SAR) echo signal is not only affected by range amplitude and phase errors introduced by nonideal THz device but also affected by motion error in azimuth induced by the nonideal trajectory of platform, resulting in the 2-D defocus of image. To address the above errors, a novel 2-D autofocusing algorithm for real airborne stripmap THz-SAR imaging is proposed. In range autofocusing, the amplitude and phase errors are estimated simultaneously in range frequency domain based on the dominant point target selected in the preliminary focused images. To avoid dividing more subapertures in azimuth error estimate, the autofocusing method based on the maximization of image contrast is adopted. Then, range-Doppler algorithm (RDA) integrated with the proposed 2-D autofocusing is performed to obtain the focused images. The real measured data, acquired by a 0.22-THz airborne SAR system, are used to demonstrate the validity of the proposed algorithm. By comparing the imaging results, the proposed method can effectively and efficiently focus airborne stripmap THz-SAR images. [ABSTRACT FROM AUTHOR]

Accurately predicting sea surface temperature (SST) plays an important role in the study of marine ecosystems and global climate. The SST prediction problem is usually formulated as a time-series regression problem; i.e., the future SST is predicted based on the historical SST. However, the existing methods are typically devoted to modeling the highly nonlinear temporal correlations in SST data. They often ignore the dynamic spatial correlations that exist. This can limit the performance of these models, making accurately predicting SST challenging. To address this challenge, by combining graph neural networks (GNNs) that have a clear advantage in modeling spatial correlations, we propose a global spatiotemporal graph attention network (GSTGAT). Specifically, we capture the global dynamic spatial correlations of nodes through a global graph attention network (GGAT) module that fuses the static adjacency matrix learned adaptively by the graph learning (GL) module with the dynamic attention coefficients. A gated temporal convolutional network (GTCN) module is used to capture the nonlinear temporal correlations. Then, the above modules are integrated into a unified neural network to predict SST. We conduct experiments on multiple time-scale datasets in the Bohai Sea and the South China Sea. The experimental results show that GSTGAT achieves the best performance and consistently outperforms other methods for different prediction horizons in the two sea areas. [ABSTRACT FROM AUTHOR]

Over the past decade, many low-rank models, factorizations, or approximations have been applied to the restoration of hyperspectral images (HSIs) (e.g., denoising, inpainting, and super-resolution) from their incomplete and/or noisy measurements. Recently, deep learning (DL) has been shown to be a powerful method for solving inverse problems (including HSI restoration), but a large amount of training data is required. Since this is not possible for HSIs, unlike red green blue (RGB) images, in this work, a novel unsupervised framework for hyperspectral inpainting (HI) is proposed that can be implemented using an untrained convolutional neural network (CNN) for deep image prior (DIP), together with a recently reported differentiable regularization for the data rank and $\ell _{2}$ -norm squared loss function. Based on the proposed framework, we come up with a novel HI algorithm [denoted as deep low-rank hyperspectral inpainting (DLRHyIn)] and a robust DLRHyIn (denoted as R-DLRHyIn) which is robust against outliers, where the latter differs from the former only in the Huber loss function (HLF) (which has been justified robust to mixed noise) used instead. Then some simulation results and real-data experiments are provided to demonstrate the effectiveness of the proposed DLRHyIn and R-DLRHyIn. Finally, we draw some conclusions. [ABSTRACT FROM AUTHOR]

Generative models have received considerable attention in signal processing and compressive sensing for their ability to generate high-dimensional natural image using low-dimensional codes. In the context of compressive sensing, if the unknown image belongs to the range of a pretrained generative network, then we can recover the image by estimating the underlying compact latent code from the available measurements. In practice, however, a given pretrained generator can only reliably generate images that are similar to the training data. To overcome this challenge, a number of methods have been proposed recently to use untrained generator structure as prior while solving the signal recovery problem. In this paper, we propose a similar method for jointly updating the weights of the generator and latent codes while recovering a video sequence from compressive measurements. We use a single generator to generate the entire video. To exploit the temporal redundancy in a video sequence, we use a low-rank constraint on the latent codes that imposes a low-dimensional manifold model on the generated video sequence. We evaluate the performance of our proposed methods on different video compressive sensing problems under different settings and compared them against some state-of-the-art methods. Our results demonstrate that our proposed methods provide better or comparable accuracy and low computational and memory complexity compared to the existing methods.

Emerging trends like the Internet of Things require an increasing number of different sensors, actuators and electronic devices. To enable new applications, such as wearables and electronic skins, flexible sensor technologies are required. However, established technologies for the fabrication of sensors and actuators, as well as the related packaging, are based on rigid substrates, i.e., silicon wafer substrates and printed circuit boards (PCB). Moreover, most of the flexible substrates investigated until now are not compatible with the aforementioned fabrication technologies on wafers due to their lack of chemical inertness and handling issues. In this presented paper, we demonstrate a conceptually new approach to transfer structures, dies, and electronic components to a flexible substrate by lift-off. The structures to be transferred, including the related electrical contacts and packaging, are fabricated on a rigid carrier substrate, coated with the flexible substrate and finally lifted off from the carrier. The benefits of this approach are the combined advantages of using established semiconductor and microsystem fabrication technologies as well as packaging technologies, such as high precision and miniaturization, as well as a variety of available materials and processes together with those of flexible substrates, such as a geometry adaptivity, lightweight structures and low costs. [ABSTRACT FROM AUTHOR]

Advanced packaging solutions require insulation and passivation materials with exceptional properties which can also fulfill the reliability needs of electronics devices such as MEMS, sensors or power modules. Since bonding (cohesive/adhesive) properties of packaging coatings are very important for reliable functioning of electronics devices, the bonding of aliphatic fluorinate-4 (AF4) parylene coatings was assessed in this work. As there is a lack of data regarding its bonding towards different substrates, pull-off tests of 1.6 and 2.5 μm thick AF4 coatings on silicon (Si) and glass (SiO2) substrates were performed. These showed a clear difference in the pull-off F/s curves between the AF4 coatings on Si and SiO2 substrates. This difference is parameterized by the pull-off energy, which will be presented in this work. To further understand the origin of the distinction in the pull-off energies between the AF4-Si and AF4-SiO2 samples and subsequently the cohesive/adhesive properties, mechanical and structural characterization was conducted on the AF4 coatings, where a clear difference in the E-modulus and crystallinity was observed. The Si and SiO2 wafers were shown to facilitate the CVD growth of the AF4 film distinctively, which likely relates to the divergent thermal properties of the substrates. Understanding of the cohesive/adhesive properties of AF4 coatings on different substrate materials advances the usage of the AF4 in electronics packaging technologies. [ABSTRACT FROM AUTHOR]

A simple yet effective approach for programmable gain amplifier (PGA) with a high precision decibel (dB)-linear characteristic and wide bandwidth is presented in this brief. This novel approach is presented by innovatively combining two techniques. Based on the binary-weighted switching technique, the circuit can approach an accurate pseudo-exponential function without using an additional exponential generator for gain control. By adopting the active feedback circuit structure, the bandwidth of PGA can be extended and it also contributes to improve the robustness, both of which cleverly reached a good consistency. The proposed PGA is fabricated in a $0.18~{\mu }\text{m}$ CMOS technology, the core circuit occupies an area of 0.11 mm2. Operated at a supply voltage of 1.8 V, the PGA consumes 8.21 mW. The measurement results show that the voltage gain of the proposed PGA can be controlled from −4.2 dB to 25.9 dB with a gain error less than ±0.09 dB, and the minimum −3dB bandwidth is almost 630 MHz. The input 1dB compression point is measured from −28.7 to 0.5 dBm, while the input referred noise is from 22.4 to 70.9 nV/ ${{\sqrt {}}}$ Hz. [ABSTRACT FROM AUTHOR]

Breakthroughs in unsupervised domain adaptation (uDA) can help in adapting models from a label-rich source domain to unlabeled target domains. Despite these advancements, there is a lack of research on how uDA algorithms, particularly those based on adversarial learning, can work in distributed settings. In real-world applications, target domains are often distributed across thousands of devices, and existing adversarial uDA algorithms – which are centralized in nature – cannot be applied in these settings. To solve this important problem, we introduce FRuDA: an end-to-end framework for distributed adversarial uDA. Through a careful analysis of the uDA literature, we identify the design goals for a distributed uDA system and propose two novel algorithms to increase adaptation accuracy and training efficiency of adversarial uDA in distributed settings. Our evaluation of FRuDA with five image and speech datasets show that it can boost target domain accuracy by up to 50% and improve the training efficiency of adversarial uDA by at least $11\times$ 11 × . [ABSTRACT FROM AUTHOR]

Metal-oxide (MOX) gas sensors commonly rely on custom packaging solution. With an ever-increasing demand for MOX gas sensors, there is a clear need for a low cost, compact and high-performance package. During normal operation, MOX sensors are heated up to a temperature in the typical range of 200-300°C. However, the generated heat must not damage or degrade any other part of the assembly. Using 3D finite elements modelling, we developed an optimal package configuration. To thermally insulate the assembly from the heated MOX sensor we have developed in-house a low thermal conductivity xerogel-epoxy composite with 22.7% by weight xerogel and a thermal conductivity of 107.9 mW m−1 K−1 which is a reduction exceeding 30% compared to commercially available epoxy. Based on the low thermal conductivity xerogel-epoxy composite, we have developed a novel packaging approach that can suit the large family of MOX sensors. The developed alternative packaging solution includes a small number of assembly steps and uses standard processes and techniques. The assembled MOX sensor is low cost and has a low power consumption, while all thermally sensitive assembly parts remain at low temperature during the system’s lifetime.

In AlGaN/GaN high electron mobility transistors (HEMTs), the long-term operation of the device is adversely affected by threshold voltage (${\text{V}_{\text{th}}}$) instability and current collapse. In this letter, using structural and electrical analyses, the impact of trapping and fluorine (F) inclusion on the device operation is scrutinized. It is found that SiNx interfacial layer significantly reduced the formation of defects, during the ohmic annealing process. Moreover, the incorporation of F ions into GaN bulk, during the gate etch process, triggers the virtual gate phenomenon. This effect has also been mitigated via the pre-gate annealing (PGA) process. As a result of these modifications, a stable operation with minimized lag performance has been achieved. [ABSTRACT FROM AUTHOR]

This article presents a neural-recording IC with automatic gain control (AGC) according to the input signal level. AGC enhances the dynamic range (DR) of the recording IC by more than 30 dB and allows it to take the benefits of the front-end amplification-based and direct-conversion-based recording structures concurrently. By adaptively controlling the analog front-end (AFE) gain, the input-referred noise (IRN) of the overall system is greatly reduced while ensuring a wide DR. A continuous-time (CT) dynamic-zoom $\Delta \Sigma $ ADC (CT-Zoom-ADC) is used for power-efficient two-step conversion. The coarse conversion output is reused for AGC, and the fine conversion resolution is adjusted adaptively by modifying the oversampling ratio according to the varying AFE gain. The presented neural-recording IC achieves 99.5-dB DR and 6.1- $\mu \text{V}_{\textrm {rms}}$ IRN over 5-kHz bandwidth, resulting in FoMDR of 185.2 dB, the effective number of bits (ENOB) of 11.4 bits, and tolerance against artifacts with differential voltage amplitudes up to 1.6 $\text{V}_{\text {pp}}$. Measurements with pulsatile artifacts and experiments in vivo validate that the proposed IC is applicable to the closed-loop neural interface. [ABSTRACT FROM AUTHOR]

Coarse-grained reconfigurable architecture (CGRA) is a promising programmable hardware with high power-efficiency and high performance. However, compiling and optimizing loops with irregular branches on CGRAs is a challenge to fulfill the performance potential. Existing predication techniques, such as partial predication (PP) and full predication (FP), conservatively implement software pipeline with a static initiation interval (II) obtained from the maximum graph, and thus only parts of the graph in each loop iteration will be actually executed, resulting in underexploited performance. To exploit more loop-level parallelism for irregular branches, this article proposes a novel dynamic-II pipeline (DIP) scheme, which realizes a pipeline with variable II by accommodating multiple iterations of short path in one static configuration. Since the DIP scheme is effective to only certain types of branches, this article designs a hybrid compilation framework integrating other complementary methods, which selects the appropriate method for source programs according to a proposed performance evaluation model. Experimental results show that: 1) the hybrid compilation framework can effectively extract branch features, correctly choose and implement corresponding branch processing methods within acceptable compile time and 2) as compared to PP and FP, DIP brings a significant total execution time (TET) reduction by 27.21% and 22.04% on average when the execution probability of a short branch is 50%. [ABSTRACT FROM AUTHOR]

This article describes how to form a SAR image without proper motion quantities. That is within the scope of factorized geometrical autofocus (FGA). The FGA algorithm is a fast-factorized back-projection formulation with adjustable geometry parameters. Subapertures are tuned and merged pair-by-pair (base-2) and step-by-step. With this technique, we can correct an erroneous geometry and form a focused image. The FGA algorithm has been applied on two datasets, acquired by the UWB CARABAS 3 system at UHF-band. The tracks are measured accurately by means of a DGPS. We however adopt and modify a geometry model. Equidistant linear tracks at fixed altitudes are assumed. These tracks are then regulated via a two-stage search strategy and a reverseprocessing procedure. As this is a first experiment at UHF-band, we provide GPS-based length values for the subapertures, to simplify the search. Multiple geometry solutions are tested for each subaperture pair, i.e., at each resolution level. Resulting FGA images are compared to reference images and verified to be focused. This indicates that it is feasible to form a focused image with wavelength resolution at UHF-band, i.e., with minimum support from a motion measurement system. [ABSTRACT FROM AUTHOR]

To evaluate the earthquake-resistance capability of an entire substation, which is a complex multi-input-multi-output system (MIMOS) composed of many various types of electrical equipment interconnected by conductors, a flow-based analysis model integrating the merit of the flow block diagram and state tree method was introduced. Two performance indicators of a substation were defined from different stakeholders, one is the expected total power transmission capacity and the other is the power accessibility to the targeted users from the substation. And directed graph logic system analysis models were developed on the Simulink platform based on the original internal logic relationship among distributed components and the power delivery path in the substation with different function types. Afterward, the defined reliability indicators associated with the overall system under a certain seismic intensity level can be obtained by combining the seismic vulnerability curve at the equipment level through the Monte Carlo simulation method. The feasibility and accuracy of the proposed system vulnerability analysis model were verified by comparison with the analytical solution through the illustrative simple MIMOS. Then A case study on a practical 220/110 kV substation was conducted, the analysis results can assist decision-makers in seismic optimization of different retrofitting measures and distribution plans for substations. [ABSTRACT FROM AUTHOR]

The hyperspectral image super-resolution (HSISR) task has been widely studied, and significant progress has been made by leveraging the deep convolution neural network (CNN) techniques. Nevertheless, the scarcity of training images hinders the research progress of the HSISR task. Moreover, the differences in imaging conditions and the number of spectral bands among different datasets make it very difficult to construct a unified deep neural network. In this article, we first present a nontraining-based HSISR method based on deep prior knowledge, which captures the image before restoring the high-resolution image by using the intrinsic characteristics of CNN. Then, we append a special network input processing module (IPM) onto the HSISR network to automatically adjust the structure of the input so that the choice of network structure is no longer limited, while the network design focuses on exploiting the spatial information of hyperspectral images (HSIs) and the correlation between spectral bands, making the method more suitable for HSISR tasks and greatly extending its applications. Extensive experimental results on the HSI datasets illustrate the effectiveness of the proposed method, and we have got comparable results with the state-of-the-art methods while requiring no training samples. [ABSTRACT FROM AUTHOR]

The reliable peeling-off and picking-up of thin chips from adhesive tape is still one of the crucial techniques in electronics packaging. The fracture of thin chip induced by high cracking stress, and the chip deviation after needle ejection in chip peeling-off stage have greatly affected the success ratio of chip pick-up processes. In this paper, a spring-buffer chip peeling technology is proposed, where the pick-up head is assigned to come in contact with the chip to avoid the chip deviation, and a spring is designed in pick-up heads which can prevent the chip cracking due to the buffer effect. Here, an effective theoretical chip–adhesive–substrate model considering the spring effect is presented to address the chip peeling analysis and competing fracture behavior between interfacial delamination and chip crack. The analytical results show that the spring-buffer chip peeling technology using the pick-up head with a spring can ensure chip peeling and restrain chip cracking under a large ejecting needle force as it can reduce the stress concentration by the needle tip. Additionally, the three-dimensional finite-element model and experiments are completed to verify the analytical results, and the paper introduces a process window for reliable chip peeling. [ABSTRACT FROM AUTHOR]

This brief reviews the principle and design of dynamic range (DR) enhancement techniques for artifact-tolerant biopotential-acquisition ICs. In order to record small input signals without being disturbed by large artifacts, which may arise from motion or stimulation, biopotential-acquisition ICs for wearable devices and bidirectional neural interfaces should have wide DR in addition to low noise and high power efficiency. This review discusses key features of DR enhancement techniques based on three topologies: (1) saturation-free instrumentation amplifier with analog-to-digital converter (ADC), (2) signal-to-noise-and-distortion ratio (SNDR) enhanced ADC, and (3) DR-enhanced ADC with moderate linearity, which are used in state-of-the-art artifact-tolerant biopotential-acquisition ICs. [ABSTRACT FROM AUTHOR]

Electromyography (EMG) is one of the most common methods to detect muscle activities and intentions. However, it has been difficult to estimate accurate hand motions represented by the finger joint angles using EMG signals. We propose an encoder-decoder network with an attention mechanism, an explainable deep learning model that estimates 14 finger joint angles from forearm EMG signals. This study demonstrates that the model trained by the single-finger motion data can be generalized to estimate complex motions of random fingers. The color map result of the after-training attention matrix shows that the proposed attention algorithm enables the model to learn the nonlinear relationship between the EMG signals and the finger joint angles, which is explainable. The highly activated entries in the color map of the attention matrix derived from model training are consistent with the experimental observations in which certain EMG sensors are highly activated when a particular finger moves. In summary, this study proposes an explainable deep learning model that estimates finger joint angles based on EMG signals of the forearm using the attention mechanism. [ABSTRACT FROM AUTHOR]

Phase retrieval (PR) is about reconstructing a signal from the magnitude of a number of its complex-valued linear measurements. Recent rapid progress has been made on the development of neural network (NN) based methods for PR. Most of these methods employ pre-trained NNs for modeling target signals, and they require collecting large-scale datasets with ground-truth signals for pre-training, which can be very challenging in many scenarios. There are a few unsupervised learning methods employing untrained NN priors for PR which avoid using external datasets; however, their performance is unsatisfactory compared to pre-trained-NN-based methods. This paper proposes an unsupervised learning method for PR which does not rely on pre-trained NNs while providing state-of-the-art performance. The proposed method trains a randomly-initialized generative NN for signal reconstruction directly on the magnitude measurements of a target signal, which approximates the minimum mean squared error estimator via dropout-based model averaging. Such a model-averaging-based approach provides a better internal prior for the target signal than existing untrained-NN-based methods. The experiments on image reconstruction demonstrate both the advantage of our method over existing unsupervised methods and its competitive performance to pre-trained-NN-based methods. [ABSTRACT FROM AUTHOR]

As the in-pixel source-follower (SF) gate size scales down in CMOS image sensors (CISs) and quanta image sensors (QISs), the pixel conversion gain (CG) increases at the cost of more 1/ ${f}$ noise. In this article, a multigate SF (MGSF) is proposed to simultaneously increase pixel CG and reduce 1/ ${f}$ noise. The MGSF improves the tradeoff between 1/ ${f}$ noise and CG that exists for pixels with conventional SFs, leading to reduced input-referred read noise at deep sub-electron levels. [ABSTRACT FROM AUTHOR]

We study the design and optimization of the Boltzmann machine hardware using superconducting circuits as a new stochastic information processing method. The Boltzmann machine is an artificial neural network of stochastic binary models wherein the energy function is determined by the given set of parameters, and the output is obtained by stochastic state transitions of the system to the energy stable states according to the Boltzmann distribution. By adjusting the set of parameters, arbitrary functions can be embedded in energy-stable states, which have applications in data dimensionality reduction and generative models. The hardware of a Boltzmann machine using superconducting circuits consists of quantum flux parametrons (QFPs), one of the superconducting circuits, magnetically coupled to each other. In this study, we designed the Boltzmann machine hardware in which logic gates such as NOR are embedded in energy-stable states. Furthermore, we applied maximum likelihood estimation (MLE), machine learning method, as an operating-point optimization method, and confirmed the effectiveness of this method in experiments. [ABSTRACT FROM AUTHOR]

This work addresses the resolution of penalized least-squares problems using the proximal gradient algorithm (PGA). PGA can be accelerated by preconditioning strategies. However, typical effective choices of preconditioners may correspond to intricate matrices that are not easily inverted, leading to increased complexity in the computation of the proximity step. To relax these requirements, we propose an unmatched preconditioning approach where two metrics are used in the gradient step and the proximity step. We provide convergence conditions for this new iterative scheme and characterize its limit point. Simulations for tomographic image reconstruction from undersampled measurements show the benefits of our approach for various simple choices of metrics. [ABSTRACT FROM AUTHOR]

Microacoustic radio frequency filters are essential electronic components for all devices using wireless communication. For miniaturization and cost reduction it is necessary to further reduce the area for the electrical contacts on the chip. CPB (copper pillar bump) interconnections on radio frequency filters can release valuable chip design area of up to 26 % compared to standard SB (solder bump) interconnections on a Qualcomm TFAP (Thin Film Acoustic Package). We developed a process to deposit CPB interconnections on microacoustic radio frequency filters. A 1.20 mm $\times1.60$ mm SAW (surface acoustic wave) LTE (Long Term Evolution) band 26 duplexer with a height of 0.20 mm including the CPB interconnections was investigated. Unbiased highly accelerated stress test, damp heat steady state, dry heat, and temperature cycling reliability tests have shown that the new interconnection technology fulfills the reliability requirements of the smart device industry. Two failure modes caused by extended temperature cycling were identified via scanning acoustic microscopy and scanning electron microscopy analyses on cross sections.

This paper presents a 60 GHz millimeter-wave (mm-wave) antenna array using standard printed circuit board (PCB) for 3D Antenna-in-package (AiP) implementation. The array consists of a 4 microstrip patch elements, differentially fed with an open stub matching feed network to enable 3D integration. The $1\times 4$ finite antenna array with ball grid array (BGA) and silicon (Si) interposer operates from 58.46 to 62.14 GHz with 3.6 GHz instantaneous bandwidth, low mutual coupling of about $1\times 4$ array consists of two substrates and one bondply layer with antennas, via-to-open stub matching network, and a differential to single-ended corporate feed network for the measurement. A prototype with a differential to single-ended corporate feed network was fabricated and tested showing a gain of about 10.02 dBi at the operating frequency with $\geq 90$ % radiation efficiency. Such a gain and efficiency make the presented design a leading candidate for 3D AiP applications.

Spaceborne synthetic aperture radar (SAR) systems, operating at L-band or lower, are very susceptible to ionospheric scintillation. The scintillation phase error (SPE) can bring about serious azimuth decorrelation and resolution loss. Compared with the motion phase error, the SPE takes on a much stronger spatial-variation both in along-track and across-track orientations, and its anisotropic feature makes the compensation more intractable. In this article, the phase gradient autofocus (PGA) is used for the first time to compensate the spatial-varying and anisotropic SPE for spaceborne SAR images. It begins with achieving a reliable SPE estimate by applying PGA to a strong scatterer. According to the preknowledge of the phase screen altitude and the anisotropic elongation heading, the emphasis is then on estimating the SPEs for other pixels from the well-estimated SPE, based on the mutual correlation and overlapping of SPEs. The pixel-by-pixel strategy is adopted for the final compensation. The proposed methodology is very appropriate for SAR images with few corner reflectors, which is validated on a simulated L-band stripmap data and a PALSAR-2 spotlight real data. It shows more powerful utility than the classical PGA. The performance analysis is implemented with regard to two necessary factors, which further confirms the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

PetscSF, the communication component of the Portable, Extensible Toolkit for Scientific Computation (PETSc), is designed to provide PETSc's communication infrastructure suitable for exascale computers that utilize GPUs and other accelerators. PetscSF provides a simple application programming interface (API) for managing common communication patterns in scientific computations by using a star-forest graph representation. PetscSF supports several implementations based on MPI and NVSHMEM, whose selection is based on the characteristics of the application or the target architecture. An efficient and portable model for network and intra-node communication is essential for implementing large-scale applications. The Message Passing Interface, which has been the de facto standard for distributed memory systems, has developed into a large complex API that does not yet provide high performance on the emerging heterogeneous CPU-GPU-based exascale systems. In this article, we discuss the design of PetscSF, how it can overcome some difficulties of working directly with MPI on GPUs, and we demonstrate its performance, scalability, and novel features. [ABSTRACT FROM AUTHOR]

Low-light images suffer from low contrast and unclear details, which not only reduces the available information for humans but limits the application of computer vision algorithms. Among the existing enhancement techniques, Retinex-based and learning-based methods are under the spotlight today. In this paper, we bridge the gap between the two methods. First, we propose a novel “generative” strategy for Retinex decomposition, by which the decomposition is cast as a generative problem. Second, based on the strategy, a unified deep framework is proposed to estimate the latent components and perform low-light image enhancement. Third, our method can weaken the coupling relationship between the two components while performing Retinex decomposition. Finally, the RetinexDIP performs Retinex decomposition without any external images, and the estimated illumination can be easily adjusted and is used to perform enhancement. The proposed method is compared with ten state-of-the-art algorithms on seven public datasets, and the experimental results demonstrate the superiority of our method. Code is available at: https://github.com/zhaozunjin/RetinexDIP. [ABSTRACT FROM AUTHOR]

Hyperspectral pansharpening aims to synthesize a low-resolution hyperspectral image (LR-HSI) with a registered panchromatic (PAN) image to generate an enhanced HSI with high spectral and spatial resolution. Recently, the proposed HS pansharpening methods have obtained remarkable results using deep convolutional networks (ConvNets), which typically consist of three steps: 1) upsampling the LR-HSI; 2) predicting the residual image via a ConvNet; and 3) obtaining the final fused HSI by adding the outputs from first and second steps. Recent methods have leveraged deep image prior (DIP) to upsample the LR-HSI due to its excellent ability to preserve both spatial and spectral information, without learning from large datasets. However, we observed that the quality of upsampled HSIs can be further improved by introducing an additional spatial-domain constraint to the conventional spectral-domain energy function. We define our spatial-domain constraint as the $L_{1}$ distance between the predicted PAN image and the actual PAN image. To estimate the PAN image of the upsampled HSI, we also propose a learnable spectral response function (SRF). Moreover, we noticed that the residual image between the upsampled HSI and the reference HSI mainly consists of edge information and very fine structures. In order to accurately estimate fine information, we propose a novel overcomplete network, called HyperKite, which focuses on learning high-level features by constraining the receptive from increasing in the deep layers. We perform experiments on three semisynthetic and one real HSI datasets to demonstrate the superiority of our DIP-HyperKite over the state-of-the-art pansharpening methods. The deployment codes, pretrained models, and final fusion outputs of our DIP-HyperKite and the methods used for the comparisons will be publicly made available at https://github.com/wgcban/DIP-HyperKite.git. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new partial Gaussian approximation (PGA) soft demapper for the core layer (CL) signal in the multiple-input-multiple-output layered-division-multiplexing (MIMO-LDM) of ATSC 3.0 systems. Unlike a conventional GA demapper, where enhanced layer (EL) signals are intentionally modelled as additive Gaussian noise, the proposed PGA jointly decodes the CL signal by assuming the EL signals to be QPSK signals added with the Gaussian noise irrespective of their modulation order and type. The assumed QPSK constellations are used in such a way that the residual signal power in the EL signals should be minimized. We verify the performance gains from this new PGA demapper by analyzing the variation of the received injection-level with respect to MIMO channel gains. Simulation results show that this new PGA outperforms conventional GA at the cost of an acceptable increase in decoding complexity, especially when dealing with low injection-levels or high code rates. [ABSTRACT FROM AUTHOR]

This brief presents an analog baseband (ABB) circuit in a 0.13 $\boldsymbol{\mu }\text{m}$ SiGe technology for transmitter leakage cancellation and close-in blocker suppressions in fully duplex (FD) frequency-modulated continuous-wave (FMCW) radar. This ABB comprises a programmable gain amplifier (PGA) and a cascaded LPF/Notch hybrid, which incorporates a closed-loop (CL) reconfigurable low-pass filter (LPF) and an open-loop (OL) tunable notch filter. The adopted key topologies include active-R-C bi-quads and $\text{G}_{\mathrm{ m}}$ -C ones. In an FD FMCW transceiver, Tx leakage and close-in blockers are difficult to be eliminated in the RF domain, especially when leakage/blockers are very close to desired signals or even in-band in the frequency domain. This LPF/notch hybrid is proposed to solve this issue. The LPF and PGA provide bandwidth (BW)/gain programmability, while the $\text{G}_{\mathrm{ m}}$ -C bi-quad provides adaptable center frequency for a notch filter. With this adaption, the notch filter could be adjusted to match the leakage/blocker offset frequency. Thus, digitally discrete programmability and analog continuous tuning capability are combined in this solution for improving the overall front-end interference robustness without de-sensitizing the Rx. Furthermore, the order of LPF/notch hybrid is programmable from 2 to 10 with a step of 2 for different rejection levels of interferences. The measured chip achieves a −3dB bandwidth of 6 ~ 21 MHz with 4-bit digital control and 1 MHz/step programmability, and a voltage gain of 0 ~ 70 dB with 9-bit digital control (3-bit from pre-amplifier, and 6-bit from PGA with 1 dB/step). With the condition of 15 dB gain, output P−dB is 11.8 dBm@3MHz, and the output IP3 is 20.8 dBm@3MHz. [ABSTRACT FROM AUTHOR]