Digital Chip Research Articles

Research on non-destructive and rapid detection technology of foxtail millet moisture content based on capacitance method and Logistic-SSA-ELM modelling.

Moisture content testing of agricultural products is critical for quality control, processing efficiency and storage management. Testing foxtail millet moisture content ensures stable foxtail millet quality and helps farmers determine the best time to harvest. A differential capacitance moisture content detection device was designed based on STM32 and PCAP01 capacitance digital converter chip. The capacitance method combined with the back-propagation(BP) algorithm and the extreme learning machine(ELM) algorithm was chosen to construct an analytical model for foxtail millet moisture content, temperature, and volume duty cycle. This work performs capacitance measurements on foxtail millet with different moisture contents, temperatures, and proportions of the measured substance occupying the detection area (that is, the volumetric duty cycle). On this foundation, the sparrow search algorithm (SSA) is used to optimize the BP and ELM models. However, SSA may encounter problems such as falling into local optimization solutions due to the reduction of population diversity in the late iterations. As a consequence, Logistic algorithm is introduced to optimize SSA, making it more appropriate for solving specific problems. Upon comparative analysis, the model predicted using the Logistic-SSA-ELM algorithm was more accurate. The results indicate that the predicted values of prediction set coefficient of determination (RP), prediction set root mean square error (RMSEP) and prediction set ratio performance deviation (RPDP) were 0.7016, 3.7150 and 1.4035, respectively. This algorithm has excellent prediction performance and can be used as a model for detection of foxtail millet moisture content. In view of the important role of foxtail millet moisture content detection in acquisition and storage, it is particularly important to study a nondestructive and fast online real-time detection method. The designed capacitive sensor with differential structure has well stabilization and high accuracy, which can be further studied in depth and gradually move towards the general trend of agricultural development of smart agriculture and precision agriculture.

Development of a self-powered digital LAMP microfluidic chip (SP-dChip) for the detection of emerging viruses.

Point-of-care (POC) diagnostics have emerged as a crucial technology for emerging pathogen detections to enable rapid and on-site detection of infectious diseases. However, current POC devices often suffer from limited sensitivity with poor reliability to provide quantitative readouts. In this paper, we present a self-powered digital loop-mediated isothermal amplification (dLAMP) microfluidic chip (SP-dChip) for the rapid and quantitative detection of nucleic acids. The SP-dChip utilizes a vacuum lung design to passively digitize samples into individual nanoliter wells for high-throughput analysis. The superior digitization scheme is further combined with reverse transcription loop-mediated isothermal amplification (RT-LAMP) to demonstrate dLAMP detection of Zika virus (ZIKV). Firstly, the LAMP assay is loaded into the chip and passively digitized into individual wells. Mineral oil is then pipetted through the chip to differentiate each well as an individual reactor. The chip did not require any external pumping or power input for rapid and reliable results to detect ZIKA RNA as low as 100 copies per μL within one hour. As such, this SP-dChip offers a new class of solutions for truly affordable, portable, and quantitative POC detections for emerging viruses.

Rapid Manufacturing of Glass‐Based Digital Nucleic Acid Amplification Chips by Ultrafast Bessel Pulses

Advanced nucleic acid amplification techniques require separating test samples into numerous units (digitalization), which enables higher sensitivity and accuracy than traditional methods. Reagent partition tools are commercially available, but are usually complex and expensive, hindering mass adoption. Herein, the fabrication of a large‐scale micro‐through‐hole array on glass substrates for an advanced digital nucleic acid amplification technique (NAAT) is demonstrated. To meet the requirement of hole quantities (over tens of thousands) for valid nucleic acid statistics, ultrafast Bessel pulses with continuous translation of the glass substrate are applied. A single‐shot Bessel pulse can lead to a single hole, and 100 holes can be produced per second (scalable to thousands per second). Experiments using the fabricated chips show that the performance meets the NAAT requirements. This work provides a highly efficient and low‐cost scheme for reagent partitioning that promotes the wide accessibility of advanced digital NAATs as well as a variety of other applications.

Optimized FIR Filter Using Genetic Algorithms: A Case Study of ECG Signals Filter Optimization

The advancement in technology and the availability of specialized digital signal processing chips have made digital filter design and implementation more feasible in a variety of fields, including biomedical engineering. This paper makes two key contributions. First, it uses a genetic algorithm to optimize the coefficients of finite impulse response (FIR) filters. Second, it conducts a case study on using genetic algorithms to optimize FIR filters for electrocardiogram (ECG) biomedical signal noise removal. The goal of the proposed filter design approach is to achieve the desired signal bandwidth while minimizing the side lobe level and eliminating unwanted signals using a genetic algorithm. The results of a comprehensive analysis show that the genetic algorithm-based filter is more effective than conventional filter designs in terms of noise removal efficiency.

Analog Convolutional Operator Circuit for Low-Power Mixed-Signal CNN Processing Chip

In this paper, we propose a compact and low-power mixed-signal approach to implementing convolutional operators that are often responsible for most of the chip area and power consumption of Convolutional Neural Network (CNN) processing chips. The convolutional operators consist of several multiply-and-accumulate (MAC) units. MAC units are the primary components that process convolutional layers and fully connected layers of CNN models. Analog implementation of MAC units opens a new paradigm for realizing low-power CNN processing chips, benefiting from less power and area consumption. The proposed mixed-signal convolutional operator comprises low-power binary-weighted current steering digital-to-analog conversion (DAC) circuits and accumulation capacitors. Compared with a conventional binary-weighted DAC, the proposed circuit benefits from optimum accuracy, smaller area, and lower power consumption due to its symmetric design. The proposed convolutional operator takes as input a set of 9-bit digital input feature data and weight parameters of the convolutional filter. It then calculates the convolutional filter’s result and accumulates the resulting voltage on capacitors. In addition, the convolutional operator employs a novel charge-sharing technique to process negative MAC results. We propose an analog max-pooling circuit that instantly selects the maximum input voltage. To demonstrate the performance of the proposed mixed-signal convolutional operator, we implemented a CNN processing chip consisting of 3 analog convolutional operators, with each operator processing a 3 × 3 kernel. This chip contains 27 MAC circuits, an analog max-pooling, and an analog-to-digital conversion (ADC) circuit. The mixed-signal CNN processing chip is implemented using a CMOS 55 nm process, which occupies a silicon area of 0.0559 mm2 and consumes an average power of 540.6 μW. The proposed mixed-signal CNN processing chip offers an area reduction of 84.21% and an energy reduction of 91.85% compared with a conventional digital CNN processing chip. Moreover, another CNN processing chip is implemented with more analog convolutional operators to demonstrate the operation and structure of an example convolutional layer of a CNN model. Therefore, the proposed analog convolutional operator can be adapted in various CNN models as an alternative to digital counterparts.

Review and Analysis of the Characteristics of IoT Sensors

Internet of things (IoT) is the concept of data transmission network between physical objects ("things"), equipped with built-in tools and technologies for interaction with each other or with the environment. The work is devoted to the inspection and analysis of sensors, how the IoT systems are associated with environmental objects. IOT uses a wide class of measurement tools, from elementary sensors (eg, temperature, pressure, light), consumption metering devices (such as intellectual meters) to complex integrated measuring systems. The parameters of 30 types of sensors for IoT are given, 2 of them of the type of temperature and humidity sensors, 2 type of pressure sensors, 13 sensors that measure the flow and amount of gas or liquid, 2 type of accelerometers, 10 approach and movement sensors and 1 noise sensor. Among them are 4 sensors for wireless sensory networks (WSM), a single-time medical sensor for internal blood pressure measurement, calorimetric, vortex and electromagnetic flow sensors and gas or fluid, accelerometer, and memes of memes, and e-memometers, and electromagnetic measurement motion, a device of measuring noise level in the range of sound vibrations. For some sensors, there are no metrological characteristics at all, at best there is only a range of measurements and resolution. The resolution ranges from 8 bits to 16 bits with a signal sampling rate from 0.5 Hz to 48 kHz. There is even a LDC1612 digital induction chip with 28 bits. WSM sensors are designed to work with DigiMesh, LoraWan and Arduino, Raspberry. Many sensors use outdated RS 232, RS 432, I2C. Keywords: internet of things, network, review, analysis, sensors, objects, metrology.

Deep learning enhanced multiplex detection of viable foodborne pathogens in digital microfluidic chip

Culture plating is worldwide accepted as the gold standard for quantifying viable foodborne pathogens. However, it is time-consuming (1–2 days) and requires specialized laboratory and personnel. This study reported a deep learning enhanced digital microfluidic platform for multiplex detection of viable foodborne pathogens. The new method used a Time-Lapse images driven EfficientNet-Transformer Network (TLENTNet) to type and quantify the bacteria through spatiotemporal features of bacterial growth and digital enumeration of bacterial culture. First, the bacterial sample was prepared with LB medium and injected into a pre-vacuumed microfluidic chip with an array of 800 microwells to encapsulate at most one bacterium in each well. Then, a programmed sliding microscopic platform was used to scan all microwells every 15 min, capturing time-lapse images of bacterial growth within each microwell. Finally, the TLENTNet was used to facilitate bacterial typing and quantification. Under optimal conditions, this platform was able to detect four bacterial species (S.typhimurium, E. coli O157:H7, S. aureus and B. cereus) with an average accuracy of 97.72% and a detection limit of 63 CFU/mL in 7 h.

Analysis and application of key technologies in digital signal processing

With the uncreasing reform of times and the continuous advancement of my science and technology, in recent years, my overall national strength is strengthening gradually. I have entered an information age, one digital age, the use of digital signal processing is becoming more and more frequent and important. Therefore, this paper mainly analyzes the application of DSP, the digital signal processing technology show to make the exposition. Digitalization is a major trend in today's society and the main theme of the development of the times, while digital signal processor is a component born with the times. The so-called digital signal processing is to use a universal digital signal chip to extract useful information from the signal by digital calculation. In recent years, in the wake of the constantly improving of science and technology and the continuous reform and innovation of technology in China, DSP is more and more close to people's production and life, and plays an important role in practical applications. Therefore, it is necessary to discuss and analyze its development.

Integrate-and-fire circuit for converting analog signals to spikes using phase encoding * *The authors would like to thank Alexander Lenz and Susanne Junghans for their expertise and input, which was very valuable for implementing and testing the designed circuit. The authors would also like to thank the Technical University of Dresden for granting them access to the SpiNNaker 2 chip

Processing sensor data with spiking neural networks on digital neuromorphic chips requires converting continuous analog signals into spike pulses. Two strategies are promising for achieving low energy consumption and fast processing speeds in end-to-end neuromorphic applications. First, to directly encode analog signals to spikes to bypass the need for an analog-to-digital converter. Second, to use temporal encoding techniques to maximize the spike sparsity, which is a crucial parameter for fast and efficient neuromorphic processing. In this work, we propose an adaptive control of the refractory period of the leaky integrate-and-fire (LIF) neuron model for encoding continuous analog signals into a train of time-coded spikes. The LIF-based encoder generates phase-encoded spikes that are compatible with digital hardware. We implemented the neuron model on a physical circuit and tested it with different electric signals. A digital neuromorphic chip processed the generated spike trains and computed the signal’s frequency spectrum using a spiking version of the Fourier transform. We tested the prototype circuit on electric signals up to 1 kHz. Thus, we provide an end-to-end neuromorphic application that generates the frequency spectrum of an electric signal without the need for an ADC or a digital signal processing algorithm.

Digital and analysis of alarm clock design based on Multisim

With the development of technology and the times, virtual simulation technology represented by Multisim has been widely used, and simulation technology is also an indispensable part of industrial design. This article uses Multisim simulation technology to design and complete a digital electronic clock based on a 74ls160 counter, composed of a decoder, a display, a numerical comparator, and an alarm clock. In this design, digital circuits are used to display and adjust "time", "minute", and "second". By using various integrated digital chips to build circuits to achieve corresponding functions. It successfully realizes the functions of calibrating the time, reporting the time on the hour, and alarming the clock. Compared with traditional electronic clocks, it has the characteristics of accurate travel, intuitive display, and no mechanical transmission, The research in this paper will contribute to the further application of virtual simulation software in the design of small devices, and will not lay a foundation for the future industrial design process.

Facile prepared microfluidic chip for multiplexed digital RT-qPCR test.

The chip-based digital polymerase chain reaction (PCR) is an indispensable technique for amplifying and quantifying nucleic acids, which has been widely employed in molecular diagnostics at both fundamental and clinical levels. However, the previous designs have yet to achieve widespread application due to limitations in complex chip fabrication, pretreatment procedures, special surface properties, and low throughput. This study presents a facile digital microfluidic chip driven by centrifugal force for digital PCR analysis. Interestingly, regardless of the hydrophilicity or hydrophobicity of the inner chip surface, an efficient digitization process can be achieved. PCR reagents introduced into the inlet can be allocated to 9600 microchambers and subsequently isolated by the immiscible phase (silicone oil). The centrifugal priming approach offers a facile means to achieve high-throughput analysis. The design was further employed for the quantification of nucleic acids using digital PCR. The calculated result exhibited a strong correlation with the measured value at the concentrations from 1 copy/μL to 1000 copies/μL (R2 =0.99). Additionally, the chip also allowed digital multiplexed analysis, thereby indicating its potential for multi-target detection applications.

Digital-scRRBS: A Cost-Effective, Highly Sensitive, and Automated Single-Cell Methylome Analysis Platform via Digital Microfluidics.

Single-cell DNA methylation sequencing is highly effective for identifying cell subpopulations and constructing epigenetic regulatory networks. Existing methylome analyses require extensive starting materials and are costly, complex, and susceptible to contamination, thereby impeding the development of single-cell methylome technology. In this work, we report digital microfluidics-based single-cell reduced representation bisulfite sequencing (digital-scRRBS), the first microfluidics-based single-cell methylome library construction platform, which is an automatic, effective, reproducible, and reagent-efficient technique to dissect the single-cell methylome. Using our digital microfluidic chip, we isolated single cells in 15 s and successfully constructed single-cell methylation sequencing libraries with a unique genome mapping rate of up to 53.6%, covering up to 2.26 million CpG sites. Digital-scRRBS demonstrates a high capacity for distinguishing cell identity and tracking DNA methylation during drug administration. Digital-scRRBS expands the applicability of single-cell methylation methods as a versatile tool for epigenetic analysis of rare cells and populations with high levels of heterogeneity.

Clinical Performance of the Line Immunoassay, Digital Liquid Chip Method, and Chemiluminescent Immunoassay for Detecting Specific Antinuclear Antibodies.

Antinuclear antibodies (ANAs) against certain antigens are useful for identifying autoimmune disorders. Although new solid phase-based immunoassays have been developed for evaluating ANAs, the conventional line immunoassay (LIA) is commonly used in clinical practice. To compare the clinical performance of 2 newly developed methods for detecting specific ANAs with LIA. Six hundred ninety-six serum samples were collected from 559 patients with autoimmune disease (AID) and 137 controls. The samples were screened by using the LIA, digital liquid chip method (DLCM), and chemiluminescent immunoassay (CLIA) for specific ANAs. The agreement across assays and the clinical performance of each assay in diagnosing ANA-associated rheumatic diseases (AARDs) were evaluated. Almost perfect agreement was observed among all assays for anti-centromere protein B (κ = 0.85-0.97), anti-ribosome P (κ = 0.85-0.88), anti-SSA 52 (κ = 0.86-0.89), and anti-SSA 60 (κ = 0.89-0.91); moderate to substantial agreement was detected for the autoantibodies against Sm, Jo-1, ribonucleoprotein, Scl-70, and SSB (κ = 0.55-0.80). LIA exhibited better sensitivity for diagnosing AARDs, while DLCM and CLIA demonstrated higher specificity. In the subset of AIDs, especially systemic lupus erythematosus, antibody positive percentages varied greatly between assays. The 3 assays showed comparable qualitative agreement; however, the standardization of testing for ANAs remains challenging owing to intermanufacturer variations. Moreover, DLCM and CLIA exhibited better specificity in distinguishing non-AID individuals, whereas LIA was more sensitive in diagnosing AARDs.

An End-to-End Dual ASIC OFDM Transceiver for Ultrasound In-Body Communication.

Implanted medical devices need a reliable, secure and low-energy wireless communication link. Ultrasound (US) wave propagation is promising over other techniques due to its lower body attenuation, inherent security and well-studied physiological impact. While US communication systems have been proposed, they either neglect realistic channel conditions or fail to be integrated into small-scale, energy-scarce systems. Therefore, this work proposes a custom, hardware efficient OFDM modem optimized for the diverse needs of ultrasound in-body communication channels. This custom OFDM modem is implemented in an end-to-end dual ASIC transceiver: a 180nm BCD analog front end and a digital baseband chip in 65nm CMOS technology. Furthermore, the ASIC solution provides tuning knobs to increase the analog dynamic range, to update the OFDM parameters and to fully reprogram the baseband processing, necessary to adjust for the channel variability. Ex-vivo communication experiments on a 14cm thick piece of beef achieve 470kbps with BER 3e-4, while consuming 56nJ/bit and 10.9nJ/bit Tx/Rx.

A digital microfluidic chip with programmable open system actuation and enhanced optical annealing with near‐infrared light

AbstractThis work investigates a digital microfluidic chip and presents an advancement to microfluidic optical annealing methods through the application of whispering gallery mode (WGM) with near infrared excitation. Establishing a microfluidic chip with point‐of‐care capabilities, including actuation and annealing, has proven to be important. Unfortunately, poor heat absorption due to the long optical penetration depth of near infrared light creates scaling limitations for applications in optical‐based microfluidics. Through the application of WGM, the interaction length between the droplet and light is increased beyond the droplet diameter to improve heating and optical absorption. This is supported by finite‐difference time‐domain electromagnetic simulations and experimental results showing a greatly improved temperature change. Such a system is implemented in an open system digital microfluidic chip, to facilitate annealing via side illumination of droplets. The open system digital microfluidic chip is programmable for droplet actuation. The fundamental experiment of preprogrammed actuation of microdroplets is demonstrated in a 36 electrode grid. The results of annealing and actuation show potential for implementation in point‐of‐care microfluidic devices.

IL MONDO DEI <em>CHIP</em>: PROGRESSI TECNOLOGICI E VULNERABILITà DI UNA FILIERA STRATEGICA

After a brief coverage of the developments that led to the birth of microelectronics, the note describes the economic and physical laws that have guided the growth of this technological segment for over fifty years. Following the laws of Moore and Dennard, the complexity of microelectronic chips has increased dramatically, reaching the point of incorporating miniaturized components, now nanometric in size, comparable in number to the stars in a galaxy. However, during these decades microelectronics has not only pursued the path of miniaturization. Alongside digital technologies, diversified platforms have developed that allow for the creation of equally essential chips in non-digital product segments. The actors of all these production chains are distributed over the various continents and are strongly interconnected. While much of the advanced digital chip manufacturing is in Asia, most of their design resides in the United States while European companies are at the forefront of sensor, industrial and automotive chips. However, the recent pandemic crisis and rising geopolitical tensions have highlighted how the lack of just one chip can disrupt the production of essential goods. Therefore, the governments of advanced economies are implementing initiatives to logistically relocate manufacturing sites and to strengthen local technological capabilities with the aim of protecting the economies from international crises. Europe too is moving in this direction but, in order to be effective, it is increasingly urgent to solve some weaknesses in the European political construction which also in this field limit the role of our continent on the global scene.

Non-invasive detection of tumor markers in salivary extracellular vesicles based on digital PCR chips

The World Health Organization (WHO) has stated that countless cancer patients could be saved if early detection and treatment were available. However, current clinical evaluation of tumors still relies primarily on imaging examinations and tissue biopsies. These methods not only require sophisticated equipment, but also have high false positive rates or invasive problems. Here, we describe a digital polymerase chain reaction (dPCR) chip for the detection of biomarkers in salivary extracellular vesicles (SEVs), which can be used to identify markers for the early diagnosis of tumors. Based on microfluidic technology fine microstructure and microfluidics operations, this dPCR chip can accurate quantitative SEVs in a variety of tumor markers, and shows extremely strong sensitivity (10 copies). In the detection of clinical samples, the chip can effectively distinguish lung cancer cases from normal controls (P < 0.001; two-tailed t-test), and in the detection of extremely low concentration samples, it shows considerably higher precise quantitative ability than quantitative real-time polymerase chain reaction (qPCR). Overall, this study may shed new light on non-invasive early screening of tumor markers by detecting extracellular vesicle-associated markers in saliva.

Closed, one-stop intelligent and accurate particle characterization based on micro-Raman spectroscopy and digital microfluidics

Monoclonal antibodies are prone to form protein particles through aggregation, fragmentation, and oxidation under varying stress conditions during the manufacturing, shipping, and storage of parenteral drug products. According to pharmacopeia requirements, sub-visible particle levels need to be controlled throughout the shelf life of the product. Therefore, in addition to determining particle counts, it is crucial to accurately characterize particles in drug product to understand the stress condition of exposure and to implement appropriate mitigation actions for a specific formulation. In this study, we developed a new method for intelligent characterization of protein particles using micro-Raman spectroscopy on a digital microfluidic chip (DMF). Several microliters of protein particle solutions induced by stress degradation were loaded onto a DMF chip to generate multiple droplets for Raman spectroscopy testing. By training multiple machine learning classification models on the obtained Raman spectra of protein particles, eight types of protein particles were successfully characterized and predicted with high classification accuracy (93%–100%). The advantages of the novel particle characterization method proposed in this study include a closed system to prevent particle contamination, one-stop testing of morphological and chemical structure information, low sample volume consumption, reusable particle droplets, and simplified data analysis with high classification accuracy. It provides great potential to determine the probable root cause of the particle source or stress conditions by a single testing, so that an accurate particle control strategy can be developed and ultimately extend the product shelf-life.

A Flexible Amplitude Equalizing Filter Based on Spoof Surface Plasmon Polaritons

In this article, an amplitude equalizing spoof surface plasmon polaritons (AESSPPs) filter deposited on flexible substrate is proposed for the first time. This new hybrid device can realize both the characteristics of amplitude equalization and interference filtering when embedding into a wideband communication system, which brings the compensation process of gain fluctuation back to the microwave front end instead of traditional complex algorithm in digital chip. It is mainly composed of equalizing thin layer and spoof surface plasmon polaritons (SSPPs) waveguide. The equalizing thin layer is realized by ITO (Indium Tin Oxide) and periodically sputtered along the propagation path of SSPPs waveguide to dissipate EM energy at different frequencies. In order to better reveal the nature property of equalization and filtering, an equivalent circuit of AESSPPs unit cell is proposed and analyzed theoretically. The equalizing value and bandwidth can be regulated independently by surface impedance of ITO thin layer and the structural parameter of SSPPs, respectively. Finally, we fabricated and measured three AESSPPs filter prototypes with different critical parameters. All the theory, simulation and measurement results are proved to have achieved the artificial dominated equalizing curve with different equalizing value and bandwidth, which validate that the proposed filter demonstrates the desired flexibility, equalization and filtering capabilities simultaneously.

An integrated digital polymerase chain reaction chip for multiplexed meat adulteration detection.

Meat adulteration detection is a common concern of consumers. Here, we proposed a multiplex digital polymerase chain reaction method and a low-cost device for meat adulteration detection. Using a polydimethylsiloxane microfluidic device, polymerase chain reaction reagents could be pump-free loaded into microchambers (40×40 chambers) automatically. Due to the independence of multiplex fluorescence channels, deoxyribonucleic acid templates extracted from different animal species could be distinguished by one test. In this paper, we designed primers and probes for four types of meat (beef, chicken, pork, and duck) and labeled each of the four fluorescent markers (hexachlorocyclohexane [HEX], 6-carboxyfluorescein [FAM], X-rhodamine [ROX], and cyanine dyes 5 [CY5]) on the probes. Specific detection and mixed detection experiments were performed on four types of meat, realizing a limit of detection of 3 copies/µL. A mixture of four different species can be detected by four independent fluorescence channels. The quantitative capability of this method is found to meet the requirements of meat adulteration detections. This method has great potential for point-of-care testing together with portable microscopy equipment.