Programmable Circuit Research Articles

Ultrarobust, Highly Sensitive and Swinging‐Independent Droplets‐Spatial Manipulator Enabled via Laser‐Printed Magnetically Actuated Shape‐Morphing Microshutters

Spatial manipulation of liquid droplets holds great significance in various areas from laboratory research to everyday life. Unfortunately, current manipulators are limited to lower responsivity and unreliable durability together with swinging‐dependent manner. Herein, an ultrarobust, sensitive, and swinging‐independent droplet‐spatial manipulator is reported, namely, magnetically actuated shape‐morphing microshutters (MASM) enabled by laser printing and soft transfer technique. Leveraging the loading/discharging of a remote magnet, the MASM can be reversibly switched between a bending mode and an erect mode within 1 s, thereby modulating the three‐phase contact line (TCL). The underlying hydrodynamics is that B‐mode MASM renders a longer TCL and an exaggerated adhesion force (Fa), thereby pinning the surface droplets under the assistance of a magnetic field. Once the magnetic trigger is removed, the bent shutters reverse back to the E‐mode so as to grant a shorter TCL and smaller Fa. Based on this phenomenon, the droplet's capture/release can be readily realized in response to magnetic stimuli. Last but not least, by taking advantage of an optimized MASM, pattern‐free configuration and droplets on‐demand marriage and targeted chemical reaction together with programmable circuit remedy are deployed. This work provides impetus for researchers in constructing intelligent droplet manipulation systems for multidisciplinary areas.

A Capacitive Computing-In-Memory Circuit With Low Input Loading SRAM Bitcell and Adjustable ADC Input Range

We present a 9T1C SRAM cell-based capacitive computing-in-memory circuit for neural network computation. The proposed design improves tolerance against process variation with a smaller cell area compared to previous capacitive SRAM CIM designs while inheriting the advantage of capacitive SRAM CIM hardware such as the linearity in multiply-accumulate (MAC) results and suppression of the static readout current. We also demonstrate a compact and low-power ADC for CIM readout, which improves the energy efficiency significantly. Finally, we demonstrate a programmable on-chip ADC reference voltage generator circuit for adjusting the ADC input range using bitcell replica arrays. The proposed circuit reduces the ADC bit-resolution requirement by considering the distribution of MAC results, and also helps to address the effect of the parasitic bitline capacitance. Measurement results show that a 128×128 macro fabricated in a 28 nm CMOS achieves 1519.5 TOPS/W at 0.7 V.

A Broadband Active Solar Cell Meta-Surface Antenna

In this letter, a broadband active solar cell meta-surface (SCMS) antenna powered by solar energy is proposed. This active antenna is composed of a programmable frequency source circuit and a SCMS antenna. The SCMS antenna is constructed by a spade shaped monopole and two SCMSs. The two SCMSs are located above and below the spade shaped monopole. We analyzed the working mechanism of SCMS using characteristic mode analysis (CMA) method. It shows the SCMS improves the voltage standing wave ratio (VSWR) and gain of the original antenna, and raise the coverage ratio of the solar cell. The VSWR of the SCMS antenna is less than 1.5 at 2.14-3.68 GHz, less than 2 at 2.08-4.1 GHz, and the gain of the antenna increases 1.74 dB on average in the frequency band of 2.6-4.1 GHz. The solar cell coverage ratio is 75.5%, with 100% insolation. This design provides an idea to achieve an active antenna powered by solar energy, which can be used for green wireless communication.

A non-inverting single-switch buck-boost converter based LED driver

<span lang="EN-US">This paper focuses on a single-switch buck-boost (SSBB) converter for light emitting diode (LED) lighting applications. The proposed LED driver with power factor correction (PFC) shows enhanced performance in terms of input power factor, efficiency and reduced voltage stress across the switch. The presented LED driver is operated in discontinuous conduction mode (DCM). The input PFC is carried out using average current mode control (ACMC) and the controller is optimized using artificial bee colony (ABC) algorithm. A hardware prototype of 50W LED driver with ACMC for PFC is implemented using a programmable interface circuit (PIC) microcontroller. The experimental results are presented and the performance is compared with a conventional buck boost LED driver. The proposed SSBB LED driver achieves an input power factor of 0.96 with an efficiency of 98%.</span>

FPGA-driven random walk noise generation for tunable laser linewidth control

We propose a novel approach for real-time generation of random walk noise. Our approach aims to generate natural laser phase noise and construct a linewidth tunable laser. By utilizing field- programmable gate array circuits and digital-to-analog converters, we generate noise that is subsequently combined with low-frequency analog noise. This combined noise is then fed into a lithium niobate phase modulator to emulate phase fluctuations. Our method facilitates accurate control over the laser linewidth spectrum, closely approximating actual noise characteristics.

DNA-Based Signaling Networks for Transient Colloidal Co-Assemblies.

Programmable chemical circuits inspired by signaling networks in living cells are a promising approach for the development of adaptive and autonomous self-assembling molecular systems and material functions. Progress has been made at the molecular level, but connecting molecular control circuits to self-assembling larger elements such as colloids that enable real-space studies and access to functional materials is sparse and can suffer from kinetic traps, flocculation, or difficult system integration protocols. Herein, we report a toehold-mediated DNA strand displacement reaction network capable of autonomously directing two different microgels into transient and self-regulating co-assemblies. The microgels are functionalized with DNA and become elemental components of the network. The flexibility of the circuit design allows the installation of delay phases or accelerators by chaining additional circuit modules upstream or downstream of the core circuit. The design provides an adaptable and robust route to regulate other building blocks for advanced biomimetic functions.

Electrical programmable multilevel nonvolatile photonic random-access memory

Photonic Random-Access Memories (P-RAM) are an essential component for the on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion losses in data links. Emerging Phase-Change Materials (PCMs) have been showed multilevel memory capability, but demonstrations still yield relatively high optical loss and require cumbersome WRITE-ERASE approaches increasing power consumption and system package challenges. Here we demonstrate a multistate electrically programmed low-loss nonvolatile photonic memory based on a broadband transparent phase-change material (Ge2Sb2Se5, GSSe) with ultralow absorption in the amorphous state. A zero-static-power and electrically programmed multi-bit P-RAM is demonstrated on a silicon-on-insulator platform, featuring efficient amplitude modulation up to 0.2 dB/μm and an ultralow insertion loss of total 0.12 dB for a 4-bit memory showing a 100× improved signal to loss ratio compared to other phase-change-materials based photonic memories. We further optimize the positioning of dual microheaters validating performance tradeoffs. Experimentally we demonstrate a half-a-million cyclability test showcasing the robust approach of this material and device. Low-loss photonic retention-of-state adds a key feature for photonic functional and programmable circuits impacting many applications including neural networks, LiDAR, and sensors for example.

Supervise and Apprehension of Students Presence using RFID

Abstract: Student attendance is now recognised as one of the most important variables or issues that represent academic accomplishments and performance at any university, as opposed to more time-consuming and wasteful methods. Differing programmed distinguishing proof advances have been more stylish, for example, the wireless technology like Radio Frequency Identification (RFID). A broad look into and a few applications are delivered for exploiting this innovation. An RFID tag or signature is used to send data from a remote innovation known as RFID as the reason for recognising an item via radio waves to RFID for each user. The present investigation centers around proposing a RFID based Attendance Management System (AMS) and furthermore data administration framework for a scholastic area by utilizing RFID innovation notwithstanding the programmable Logic Circuit, (for example, Raspberry Pi), and PHP Server

Implementation of an Industrial Automation Remote Lab (IARL) and validation using a deep learning approach during the COVID pandemic

AbstractThe purpose of this study is to demonstrate a novel remote lab development for improving student psychomotor skills in the Industry 4.0 course. In this work, the vision and Internet of Things‐based lab setup for third‐year undergraduates is proposed as Industrial Automation Remote Lab (IARL). The major motivation of this novel approach is to replicate Industry 4.0 in the laboratory for learning purposes to undergraduate students during pandemic condition. In industries, it is essential to collect all the data from the machines to make optimal use of resources and increase the efficiency of the system. The stored data are interfaced through the user datagram protocol to control the speed of the hydraulic actuator. In the proposed experimental work, a vision sensor‐based anydesk remote control software technique is employed to conduct programmable logic circuit (PLC) experiments. To collect the various output image data sets, numerous PLC experiments were conducted. The collection of images is classified to investigate the student performance using deep learning algorithms like YOLOv4, VGG, and YOLOv5. Based on the simulation and validation results, the IARL‐based approach with YOLOv5 outperforms the other two algorithms with 98% accuracy. Additionally, the validated statistical internal and external results show that the remote laboratory Conceive, Design, Implement, and Operate (CDIO) batch performance is highly improved over virtual laboratory CDIO batch students.

Transferable learning on analog hardware.

While analog neural network (NN) accelerators promise massive energy and time savings, an important challenge is to make them robust to static fabrication error. Present-day training methods for programmable photonic interferometer circuits, a leading analog NN platform, do not produce networks that perform well in the presence of static hardware errors. Moreover, existing hardware error correction techniques either require individual retraining of every analog NN (which is impractical in an edge setting with millions of devices), place stringent demands on component quality, or introduce hardware overhead. We solve all three problems by introducing one-time error-aware training techniques that produce robust NNs that match the performance of ideal hardware and can be exactly transferred to arbitrary highly faulty photonic NNs with hardware errors up to five times larger than present-day fabrication tolerances.

Demonstration of a Bosonic Quantum Classifier with Data Reuploading.

In a single qubit system, a universal quantum classifier can be realized using the data reuploading technique. In this study, we propose a new quantum classifier applying this technique to bosonic systems and successfully demonstrate it using a silicon-based photonic integrated circuit. We established a theory of quantum machine learning algorithm applicable to bosonic systems and implemented a programmable optical circuit combined with an interferometer. Learning and classification using part of the implemented optical quantum circuit with uncorrelated two photons resulted in a classification with a success probability of 94±0.8% in the proof of principle experiment. As this method can be applied to an arbitrary two-mode N-photon system, further development of optical quantum classifiers, such as extensions to quantum entangled and multiphoton states, is expected in the future.

Real-Time Monitoring of Human Breathing Using Wearable Tilted Fiber Grating Curvature Sensors

Respiration rate and volume are important physiological indicators of human health. They can indicate cardiovascular health status and cardiopulmonary function. Here, we demonstrate a compact breath monitoring system based on a wearable tilted fiber Bragg grating (TFBG) curvature sensor for human respiration monitoring. With the assistance of a custom designed breathing tube, TFBGs provide a very reproducible highly sensitive, real time response to curvature changes during respiration. The miniaturized and cost effective sensing system which we propose consists of a tunable laser module, a field programmable gate array acquisition control circuit module, and a sensor module. Different breathing patterns can be successfully identified from the optical amplitude and frequency output of the sensor. The robust instrument displays high repeatability and real-time tracking.

A Wireless Sensor Network (WSN) Application for Smart Monitoring and Control System for Drought

The Philippine government persistently grapples with the challenges of low crop yield caused by drought, requiring efficient water management strategies. This becomes increasingly critical given the rising population and decreasing food supply. This study introduces an innovative precision agriculture system powered by the Internet of Things (IoT), designed to address this issue. This IoT-enabled system harnesses wireless technology, connecting environmental and soil sensors via a Wireless Sensor Network (WSN). These sensors provide real-time data on farmland stress conditions, enabling farmers to respond swiftly with appropriate interventions. The gathered data are stored in both local and remote database systems and relayed via WiFi to an automated device—a programmable circuit attached to a magnetic latch solenoid—to control field irrigation. The system was piloted in three 12x16 m2 plots in San Ramon, Zamboanga City, employing 24 sensor and automation boxes strategically deployed across the irrigation network. Each box, protected by a robust, 3D-printed ABS plastic casing, safeguards the intricate circuitry from harsh environmental conditions. MQTT and HTTP protocols facilitate the delivery of information to a dedicated Android Application, ensuring remote access. The data is further analyzed using a Neural Network model, optimizing the irrigation schedule intelligently. Our findings validate the efficacy of this smart farming system as a scalable and efficient solution for precision agriculture.

Low-loss skimming waveguides with controllable mode leakage for on-chip saturable absorbers

Abstract Emerging 3D photonic circuits would greatly benefit from the ability to integrate skimming waveguides with low loss and controllable inscription depth into photonic circuits. These waveguides allow for the interaction of guiding light directly with external modulation signals and enable programmable photonic circuits. Here, we report the fabrication of a novel photonic-lattice-like skimming waveguide (PLLSW) using femtosecond laser writing. Our method enables fine control of cross-sectional symmetry and writing depth of waveguides, achieving a minimum depth of 1 μm and a low insertion loss of 1 dB. Based on the PLLSW, we demonstrate on-chip light modulation by designing an evanescent-field-type saturable absorber through the coupling of a carbon nanotube film with the PLLSW, which exhibits saturation intensity from 20 to 200 MW/cm2 through the balanced twin-detector measurement. The strong nonlinear optical response of the PLLSW-based saturable absorber is further exploited to drive a Q-switched pulse laser at 1550 nm based on a fiber laser cavity. Our work demonstrates an effective method to integrate nonlinear optical materials into a glass chip for all-optical switching based on 3D waveguides, which holds great potential for the construction of large-scale programmable photonic circuits in the future.

Design and Implementation of an Orbitrap Mass Spectrometer Data Acquisition System for Atmospheric Molecule Identification

Orbitrap mass spectrometers have gained widespread popularity in ground-based environmental component analysis. However, their application in atmospheric exploration for space missions remains limited. Existing data acquisition solutions for Orbitrap instruments primarily rely on commercial systems and computer-based spectrum analysis. In this study, we developed a self-designed data acquisition solution specifically tailored for atmospheric molecule detection. The implementation involved directly integrating a spectrum analysis algorithm onto a field programmable gate array (FPGA), enabling miniaturization, real-time performance, and meeting the desired requirements. The system comprises signal conditioning circuits, analog-to-digital conversion (ADC) circuits, programmable logic circuits, and related software. These components facilitate real-time spectrum analysis and signal processing on hardware, enabling high-speed acquisition and analysis of signals generated by the Orbitrap. Experimental results demonstrate that the system can sample front-end analog signals at a rate of 25 MHz and differentiate signal spectra with an error margin of less than 7 kHz. This establishes the viability of the designed data acquisition system for atmospheric mass spectrometry analysis.

Programmable photonic integrated meshes for modular generation of optical entanglement links

Large-scale generation of quantum entanglement between individually controllable qubits is at the core of quantum computing, communications, and sensing. Modular architectures of remotely-connected quantum technologies have been proposed for a variety of physical qubits, with demonstrations reported in atomic and all-photonic systems. However, an open challenge in these architectures lies in constructing high-speed and high-fidelity reconfigurable photonic networks for optically-heralded entanglement among target qubits. Here we introduce a programmable photonic integrated circuit (PIC), realized in a piezo-actuated silicon nitride (SiN)-in-oxide CMOS-compatible process, that implements an N × N Mach–Zehnder mesh (MZM) capable of high-speed execution of linear optical transformations. The visible-spectrum photonic integrated mesh is programmed to generate optical connectivity on up to N = 8 inputs for a range of optically-heralded entanglement protocols. In particular, we experimentally demonstrated optical connections between 16 independent pairwise mode couplings through the MZM, with optical transformation fidelities averaging 0.991 ± 0.0063. The PIC’s reconfigurable optical connectivity suffices for the production of 8-qubit resource states as building blocks of larger topological cluster states for quantum computing. Our programmable PIC platform enables the fast and scalable optical switching technology necessary for network-based quantum information processors.

Field programmable gate array hardware in the loop validation of fuzzy direct torque control for induction machine drive

Introduction. Currently, the direct torque control is very popular in industry and is of great interest to scientists in the variable speed drive of asynchronous machines. This technique provides decoupling between torque control and flux without the need to use pulse width modulation or coordinate transformation. Nevertheless, this command presents two major importunities: the switching frequency is highly variable on the one hand, and on the other hand, the amplitude of the torque and stator flux ripples remain poorly controlled throughout the considered operating speed range. The novelty of this article proposes improvements in performance of direct torque control of asynchronous machines by development of a fuzzy direct torque control algorithm. This latter makes it possible to provide solutions to the major problems of this control technique, namely: torque ripples, flux ripples, and failure to control switching frequency. Purpose. The emergence of this method has given rise to various works whose objective is to show its performance, or to provide solutions to its limitations. Indeed, this work consists in validation of a fuzzy direct torque control architecture implemented on the ML402 development kit (based on the Xilinx Virtex-4 type field programmable gate array circuit), through hardware description language (VHDL) and Xilinx generator system. The obtained results showed the robustness of the control and sensorless in front of load and parameters variation of induction motor control. The research directions of the model were determined for the subsequent implementation of results with simulation samples.

DESIGN OF PROGRAMMABLE ELECTRONIC CIRCUITS WITH MID-RANGE PIC MICROCONTROLLERS FOR ROBOTICS LABORATORIES

Through this document, it was possible to analyze the main characteristics of the volume of scientific production regarding the study of the variables Electronic Circuits, Robotics Laboratory in order to know the usefulness of programmable electronic circuits within the activities carried out in robotics laboratories. A bibliometric analysis was proposed to analyze details such as Year of Publication, Country of Origin of the publication, Area of Knowledge in which the published research is carried out and the Type of Publication most frequently used by the authors of each document published in high-impact journals indexed in the Scopus database during the period between 2017 and 2022. Among the main findings, it was possible to determine that, for the execution of the different research methodologies, the report of 16 scientific documents related to the study of Electronic Circuits and Robotics Laboratory was achieved. The maximum number of publications made in a year was 3 documents submitted in 2017 as well as in 2019, 2020 and 2021. The country of origin of the institutions that reported the highest number of records in Scopus, was Italy with 3 documents, the same number presented by authors affiliated with institutions in the United States. The area of knowledge with the greatest influence at the time of executing the research projects that resulted in scientific publications was Engineering, which contributed great theoretical material in a total of 12 publications, followed by Computer Science with 9 documents. Finally, the type of publication most frequently used to publicize findings from the analysis of the aforementioned variables was the Journal Article, which represented 50% of the total scientific production.

Heavy tails and pruning in programmable photonic circuits for universal unitaries

Developing hardware for high-dimensional unitary operators plays a vital role in implementing quantum computations and deep learning accelerations. Programmable photonic circuits are singularly promising candidates for universal unitaries owing to intrinsic unitarity, ultrafast tunability and energy efficiency of photonic platforms. Nonetheless, when the scale of a photonic circuit increases, the effects of noise on the fidelity of quantum operators and deep learning weight matrices become more severe. Here we demonstrate a nontrivial stochastic nature of large-scale programmable photonic circuits—heavy-tailed distributions of rotation operators—that enables the development of high-fidelity universal unitaries through designed pruning of superfluous rotations. The power law and the Pareto principle for the conventional architecture of programmable photonic circuits are revealed with the presence of hub phase shifters, allowing for the application of network pruning to the design of photonic hardware. For the Clements design of programmable photonic circuits, we extract a universal architecture for pruning random unitary matrices and prove that “the bad is sometimes better to be removed” to achieve high fidelity and energy efficiency. This result lowers the hurdle for high fidelity in large-scale quantum computing and photonic deep learning accelerators.

Design and application of a programmable matrix determinant-solving circuit based on memristors

矩阵行列式的求解在工程应用中具有重要的意义并得到了广泛的应用，如何高效快速地求解矩阵行列式成为了物联网大数据时代的迫切需求。为此，本文提出了一种基于忆阻器的可编程模拟电路，可一步完成任意阶行列式的计算。首先，本工作给出了二阶行列式和三阶行列式的计算电路。在此基础上，通过组合电路的方式得到任意阶数行列式求解电路。同时，该电路还可以通过调节忆阻器的电导和输入电压源来实现可编程的功能。Pspice的仿真结果表明，该电路的精度在99.4%以上，运行时间比Matlab仿真快3个数量级，即便在10.0%的误差干扰的情况下，其精度仍能超过93%。相比于数字处理器，该电路在面积开销与功耗上具有显著优势。最后，该电路可通过各个卫星的余弦参数来计算多卫星构成的空间矩阵行列式以辅助定位卫星组合的选择，其计算结果的平均精度可达99.7%。