Miracle-dz/UniFAR-FTUs huggingface dataset:
Memristive CNN for Wafer defect detection

The increasing demand for a high density of transistors in the chip with high yield often requires the manufacturing process to be mature. Defects are common when a new process technology is introduced. Manual testing for each wafer during a separate fabrication stage is a time-consuming process. To improve the production quality, the wafer map obtained after the electrical probe test should be categorized into corresponding defect classes. In this paper, we propose an automated wafer defect classification system using a Convolutional neural network (CNN)-memristor crossbar structure. The weights extracted from a pretrained neural network model are deployed into a crossbar structure. The output probability values from the softmax layer determine the class to which the wafer input belongs. The performance of CNN architecture is compared with other deep learning models. The area and power requirements of the proposed hardware architecture are also presented.
Electromigration Effects in Processes of Nano- Structured Thin Films Growth

We discuss effects of electromigration in processes of nano-structured thin films growth at condensation from gaseous phase end epitaxial growth in the framework of numerical simulations. It will be shown that an increase in the strength of the electric field applied to the substrate promotes formation of percolate structures of adsorbate. We will analyze dynamics of the surface morphology change in details. This study provides insight into details of electromigration effects at self-organization of adatoms into percolating adsorbate islands during nano-structured thin films growth at both condensation from the gaseous phase and epitaxial growth of multi-layer pyramidal-like adsorbate structures.
High performance VLSI architecture for the modified SORT-N algorithm

Fast running sorting of streaming input data samples is very important in many applications such as order statistics, nonlinear filtering, MMax selective-tap adaptive filtering etc. This paper proposes a high performance VLSI architecture for the modified SORT-N algorithm for fast running sorting. Through analysis and also through synthesis results, we show that the critical path of the proposed architecture is almost independent of the sorting order N. ASIC synthesis results of the designed architecture shows that the proposed architecture has double the performance for N=1024 along with a reduction in area and power metrics compared to state-of-the-art architecture reported in literature and thus, it is potentially useful in real-time applications which have stringent throughput requirements.
A Correlated Double Sampling Technique for Charge-Sensitive Amplifiers based on TFTs

In this work is presented a low-noise Charge Sensitive Amplifier (CSA) suitable for the readout of high-impedance pyroelectric sensors. The circuit exploits a novel Correlated Double Sampling (CDS) technique which allows both continuous-time integration and cancellation of the Thin-Film Transistor (TFT) 1/f noise. The CSA has been implemented in a printed unipolar Organic TFT technology and simulation results demonstrate a Signal-to-Noise Ratio (SNR) improvement of approximately 10 dB when the proposed CDS technique is applied at the frequency of 500 Hz. This result is expected e.g., to extend the proximity detection range and improve the safety in human-machine applications using large-area infrared-sensing surfaces. Moreover, the proposed technique can be used in generic Analog Frontend Electronics embedding switched capacitor amplifiers, to suppress correlated noise sources.
SQNR-based Layer-wise Mixed-Precision Schemes with Computational Complexity Consideration

Recently, AI acceleration is critical for hardware systems from mobile/edge devices to high-performance data servers. For on-device AI, there have been many studies on hardware numerical precision reduction for low hardware complexity considering limited hardware resources of mobile/edge devices [1–2]. However, if taken too far, aggressive quantization with low precision can degrade model accuracy which is the fundamental measure of deep learning quality. With the layer-wise mixed-precision networks where the different precision scheme is applied to each layer, we can reduce off-chip memory bandwidth requirements as well as hardware complexity while retaining model accuracy [3]. However, it takes a long time to determine the optimal precision scheme for each layer due to the repetitive trainings with the different precision configurations. Also, if only the accuracy of the target neural network is considered in the process of the mixed-precision determination, it is difficult to sufficiently reduce the computational complexity, which is the most important in mobile/edge devices [4].
A Dual-Band Vector-Sum Phase Shifter for 28-GHz and 60-GHz Phased Arrays in 65-nm CMOS

This paper presents a 5-bit dual-band active phase shifter for both 28-GHz and 60-GHz phased array applications. With the inclusion of a dual-band hybrid transformer-based quadrature generator and a wide-band vector modulator, this phase shifter achieves 3-dB gain bandwidth of 27-34 GHz and 55-63 GHz. The vector modulator is composed of a balun-loaded buffer and a Gilbert cell-based variable gain amplifier (VGA). Further, a digital-to-analog convertor (DAC) controls the gain of vector modulator to achieve 5-bit phase resolution. The circuit is designed in a 65-nm CMOS technology. Post layout simulation results show that the RMS phase error is 2.27° ~ 2.94° and 1.96° ~ 3.07° and the RMS gain error is 1.17 ~ 1.53dB and 1.26 ~ 1.91dB in the 3-dB bandwidths of 27-34 GHz and 55-63 GHz, respectively. The post-layout simulated input-referred 1-dB compression point (IP1dB) is larger than −8.7dBm, while the peak gain is −0.17dB at 29 GHz with an average power consumption of all phase states of 32.4 mW. The core area of the phase shifter is 560 $\mu m\times 560 \mu$m.
SEE Sensitivity of a 16GHz LC-Tank VCO in a 22nm FinFET Technology

Voltage-controlled oscillators (VCOs) are essential components of frequency synthesizers used in space-based applications. Radiation in harsh space environments can cause SEEs (single-event effects) when a charged particle hits the silicon substrate and induces a transient current within the circuit that causes erroneous behavior. Hence, it is essential to understand the radiation sensitivity of a given process in order to design circuits that can tolerate these conditions. An LC-tank VCO is designed in a 22nm FinFET process and achieves 12.5-16.8GHz tuning range and −126dBc/Hz phase noise at a 10MHz offset from a 16.0GHz carrier. SEE sensitivity is quantified with a measured VCO maximum cross-sectional area of 350um2 when testing with heavy ions with a linear energy transfer from 10 to 90 MeV.cm2/mg. These results are pertinent for future radiation-hardened VCO designs in this process.
Research on Film and Television IP Consuming Intention of Generation Z College Students

Based on the perceived value of consumers, this paper builds a model of the consuming intention of generation Z college students to consume IP for Film and Television, and uses survey data to empirically test the influence path of each variable by structural equation. The results show that perceived usefulness, perceived entertainment and perceived ease of use positively affect the consuming attitude of Film and Television IP consumption, consuming attitude and perceived ease of use positively affect the consuming intention of Film and Television IP consumption, and perceived risk negatively affect the consuming attitude and consuming intention. Therefore, in the future, Film and Television IP enterprises should provide good user experience, improve the value connotation and quality of Film and Television IP products, control the potential risks of products, and improve the entertainment of products to increase effective supply.
Wearable Bowel Sound Monitoring with Quality Enhancement using U-net

Human bowel sounds (BSs) contain rich information on gastrointestinal health status. Recently, the wearable BS monitoring systems has made it possible to record the human BSs in the long term. However, BSs collected by long-term monitoring devices usually contain various types of noises, which significantly affect the detection accuracy on BS events of interest. To filter out the noises in the long-term BS recordings, a U-Net convolutional neural network (CNN) based BS quality enhancement algorithm is proposed. The algorithm features a frequency-domain BS enhancement using U-Net to efficiently suppress the amplitude spectrum noises of BS segments. Experimental results on our BS dataset show that after enhancing with the proposed algorithm, the average SNR improvement of the sound segments is 28.72dB, which is the best among similar works. Moreover, after the enhancement, the accuracy of BS event detection using a threshold-based approach can reach 98.38%, which proves the effectiveness of the proposed enhancement algorithm. To the best of our knowledge, we are the first to utilize the CNN for BS quality enhancement.
Topological classification of adiabatic processes

Certain band insulators allow for the adiabatic pumping of quantized charge or spin for special time dependences of the Hamiltonian. These ``topological pumps'' are closely related to two-dimensional topological insulating phases of matter upon rolling the insulator up to a cylinder and threading it with a time-dependent flux. In this paper we extend the classification of topological pumps to the Wigner-Dyson and chiral classes, coupled to multichannel leads. The topological index distinguishing different topological classes is formulated in terms of the scattering matrix of the system. We argue that similar to topologically nontrivial insulators, topological pumps are characterized by the appearance of protected gapless end states during the course of a pumping cycle. We show that this property allows for the pumping of quantized charge or spin in the weak-coupling limit. Our results may also be applied to two-dimensional topological insulators, where they give a physically transparent interpretation of the topologically nontrivial phases in terms of scattering matrices.
A Novel Approach in Edge Computing: In-Memory Sensing of Cancer Markers

We present here the first ever-reported direct computation of the cancer risk on cancer markers simultaneously detected by memristors. The novel approach in cancer diagnostics here proposed is based on the fusion of the three actions of sensing, computing and memory in a single-kind of device. So, this paper proposes the new concept of in-memory sensing as a disruptive approach in edge-computing. The concept is demonstrated by showing a well-known case in cancer diagnostics: the estimation of the risk of prostate cancer based on the simultaneous measure of Prostate Specific Antigen (PSA) and its Membrane isoform (PSMA).
Comparative Study on Error in MIMO Radar DOA Estimation

Hardware implementation for faster real-time signal processing in multiple-input multiple-output (MIMO) radar beamforming by estimating direction of arrival (DOA) using multiple signal classification (MUSIC) is attractive. The above subspace-based method works with a basic principle of eigenvalue decomposition and is found suitable for application-specific integrated circuit (ASIC), field-programmable gate array (FPGA). In that regard, the cyclic Jacobi method is used for computing eigenvalues and eigenvectors, by orthonormal plane rotations in accomplishing eigenvalue decomposition. However, the fasterness and foremost, the accuracy of the DOA estimator realized in such above hardware depends on the involved number of antenna elements in an array, the number of snapshots, and the signal-to-noise ratio. Therefore, the comparative study on DOA estimation error on the above mentioned parameters in MUSIC and QR-based algorithms is presented in this paper.
Matrix at slow roll: nonrelativistic and perturbative

We analyze the slow roll dynamics of the massive version of Yang–Mills-type matrix mechanics. We find that one can reproduce in this limit the one-loop non-Hermitian matrix model, describing the dilatations of the local gauge invariant composite operators in the scalar sector of super Yang–Mills theory. This possibility is explained through the fact that the dilatation operator of the last can be identified with the radial-time Hamiltonian. We also explore the finite mass corrections to the slow roll action.
Research and Practice of Grain Depot Ecological Precision Measurement and Control Experimental Teaching System based on 5G Edge Calculation

Implementing the Internet plus grain action plan and upgrading the level of information industry is one of the important national policies of the advanced countries in the world. To vigorously develop the informatization of grain enterprises and promote the construction of smart grain depots, we need to focus on strengthening the intelligent upgrading of grain depots and the cultivation of scientific and technological talents. At present, most of the existing ecological measurement and control systems of grain depots still use the traditional manual trigger IoT sensing and acquisition mode. The cable array has problems such as single point of failure, insufficient density, and a long time for one measurement. In addition, the heat capacity of the warehouse wall is easy to cause heat accumulation in the outer ring of the grain pile, and the temperature change lags behind. In the IoT course experiment of engineering degree graduate students in colleges and universities, there is a lack of experimental teaching links combined with engineering projects. This paper designs and implements a 5G edge computing grain depot ecological accurate measurement and control system and engineering experimental teaching platform, which can not only solve the technical difficulties to be overcome in modern grain depots, but also realize the teaching of graduate engineering experiment courses. The experimental teaching effect in the past three years shows that 35 sets of engineering experimental teaching software and hardware devices developed have played an important role in graduate course experiment and engineering training.
Design of Network Security Experiment Teaching System Based on Honeypot Technology

“IoT information security” is an introductory course in network security. Safety offensive and defense experiments are the means that students can get in contact with network attacks. Students’ network security protection awareness and information protection capabilities can be improved by offensive and defense experiments. At present, there is a problem with high update costs, insufficient comprehensive, complicated operation, and insufficient operation. A new way is presented in this paper to improve the current offensive exercise experiments, providing training platform for postgraduate industrial Internet security courses. It achieves this by using Generative Adversarial Networks (GAN) in honeypot to generate a virtual data for industrial equipment simulation, and using support vector machine (SVM), decision tree and other networks to detect intrusion data. And then a visualization platform is established for students to do experiment.
On a Generalization of Tellegen’s Theorem to Quantum Circuits

Tellegen’s theorem is one of the foundational topological theorems of circuit theory. In its most elementary form, it expresses an energy balance between the power supplied at the ports of the electrical circuit network and the power consumed in its internal branches. But the topological roots of Tellegen’s theorem allow it to take other forms that make it into a powerful tool for deriving several fundamental results in electrical circuit networks, linear or non-linear, reciprocal or nonreciprocal. Tellegen’s theorem admits a generalization to the field case of Maxwell’s equations that makes it useful in the analysis and modeling of microwave devices. In this paper, the field case of Shrödinger’s equation is examined, and a generalization of Tellegen’s theorem to the case of quantum wave functions is proposed. This generalization has the potential of extending the application domain of Tellegen’s theorem to the circuits used in quantum computing.
Amphi-PIC J-Aggregate - Protein Complexes: Stability in Blood and Toxicity to Cell Cultures

The stability of amphi-PIC J-aggregate in a complex with the blood components and their toxicity in vitro have been estimated. The intensity of absorption and luminescence bands for the J-aggregates in the blood components appeared to be higher compared with buffer solution. The same was found for the J-aggregates photostability. The low toxicity was found at low dye concentration (10−5 M and less) and low DMSO content (2% and less) with the most sensitivity for the proliferative function of the cell.
A Battery-free Pavement Roughness Estimation System Based on Kinetic Energy Harvesting

Wireless sensor network (WSN) enables the continuous monitoring of environmental conditions. These systems are usually powered by batteries. Given that there might be tremendous distributed sensor nodes in a network, battery maintenance must become one of the major challenges for their massive deployment. Energy harvesting technology, by which energy is extracted from the ambient environment, is developed for powering the ubiquitous Internet of Things (IoT) devices using different types of local energy, such as solar, vibration, and wind. In this paper, based on the vibration energy harvesting technology, we introduce a battery-free pavement roughness estimation system (BF-PRES), which provides road roughness information by linking the driving vibration and wireless packet count. Instead of harvesting energy to power an off-the-shelf commercial accelerometer and implementing some algorithms for vibration estimation, BF-PRES simply sends out a BLE Beacon packet, when the accumulated energy arrives at a sufficient level. Given that larger vibration intensity gives higher harvested power, the packet count within a constant time interval should be positively related to the road roughness. Lab testing shows the feasibility of the proposed design. In addition, this study also provides a new design scheme and easy implementation of battery-free or energy-constrained IoT systems.
Design of Variable Bit-Width Arithmetic Unit Using Single Flux Quantum Device

This paper presents the design of an ultra-high-speed, low-power arithmetic unit that supports variable bit-width operations with single flux quantum (SFQ) technology. Because of the high-speed nature of superconductor devices, we can achieve extremely high power-performance efficiency that cannot be achieved by state-of-the-art CMOS devices. To implement the complex function to support the variable bit-width feature, we introduce a novel circuit architecture to maintain the high-speed operation over 50GHz. Our prototype chip design successfully demonstrated 53.5GHz 1.59mW operations.
Concurrent Effect of Redundancy and Switching Algorithms in SAR ADCs

This paper analyses for the first time from a quantitative standpoint the effectiveness of redundancy in successive approximation register (SAR) analog-to-digital converters (ADCs) that employ the conventional and monotonic switching algorithms. It is shown that the redundancy tolerance window is one-sided in the case of conventional switching algorithm, thus only underestimations of the input signal can be corrected. Conversely, the monotonic switching algorithm shows a symmetric tolerance window that allows the correction of both underestimations and overestimations of the input signal. Thermal noise due to switches and comparator, and supply bouncing cause both these errors and they can be effectively corrected only with a symmetric redundant window. Behavioral simulations confirm that the monotonic switching algorithm applied to redundant SAR ADCs achieves better performance than the conventional one in terms of signal-to-noise-and-distortion ratio (SNDR).
2-Dimenstional Reconfigurable Reflecting Unit Cell for THz Communication

Several techniques have been used to design phase reconfigurable reflecting surfaces for specific conditions. Though, to attain wider beam switching is one of the thirst areas for terahertz communications. Here we propose a reflecting surface based on unit cell structure with PIN diode that is capable of switching beam up to 55° in azimuthal angle on 60° elevation angle. This work partially addresses the non-line of sight (NLOS) problems and allows quasi-line of sight wireless communication at 1.7 THz. Further, studied the impact of PIN diode placement and switching property on beam steerability. The proposed unit cell concept gives a wider switching angle with better angle stability.
Design of a LiDAR point cloud data processing system for power line extraction on FPGA

High-voltage power lines are essential for people’s daily life. Unmanned aerial vehicle (UAV) airborne light detection and ranging (LiDAR) is widely used in power line management. The workload of LiDAR point cloud data processing is very large, however, most of the data needs to be sent back to the local or cloud for processing, which produce great latency. In this work, an FPGA-based LiDAR point cloud data processing system for power line extraction is proposed. The original points are classified into foreground points including power lines and power towers, and background points including ground, vegetation and other points. The raw point data is grid grouped locally. Feature extraction algorithm is applied to each grid in parallel. The precision, recall and F1-score are 99.46%, 87.55%. 93.13%, respectively. The proposed system achieves a processing speed of 37.49K points per second. The entire system is integrated on a Xilinx Ultra 96 single computer board with FPGA for acceleration. The total weight of the board is 123.3 grams.
Exploiting Game Theory Strategy and Artificial Intelligent to Analyze Social Networks: A Comprehensive Survey

Connecting with new people and expanding existing social squares is only one of the many benefits individuals may get from using social networking platforms. Social networks facilitate effective communication and cooperation, provide commercial prospects, and offer substantial social benefit. Using assumption, definition, analysis, modeling, and optimization techniques, social network issue research is productive. In this research, we categorize the known challenges of game theory applied to social networks into four categories: information dissemination, behavior analysis, community discovery, and information security. Every category may be clearly mastered in terms of knowledge application. On the basis of current research, we examine the limits of game theory and suggest future paths for social network research.
Vacuum Packaging of MEMS-based Infrared Detectors

MEMS-based infrared sensor is a key component in thermal cameras. Vacuum wafer-level packaging is a cost-effective process to ensure proper functionality of such sensors over its long lifetime. This paper presents our recent work in the process development of the vacuum wafer-level packaging for a large sensor array covering an area > 100 mm2, Cu-Sn solid-liquid interdiffusion bonding was used for the hermetic sealing. A seal frame design with 3 rails showed 60% reduction in average Sn squeeze-out distance compared to a continuous seal frame. Successfully vacuum sealed sample with 300 µm thick Si cap provided mechanical stability and measurable cap deflection for a 12 mm × 12 mm die area. Cap deflection measurements of a sealed wafer sample show 14 out 19 dies retain the initial vacuum level after 8 months since bonding. Various challenges in obtaining the vacuum sealing of large dies at wafer-level are also discussed.