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Within the frame of this work, a user-friendly MATLAB program for the simulation of signals and electronic noise at the nuclear radiation readout system was developed. Signal and noise in terms of equivalent noise charge (ENC) have been evaluated. Five noise sources were considered in order to have high simulation accuracy. The role and influence of different parameters of the readout circuits on the output signal and noise were predicted and illustrated. Moreover, the effect of all elementary parameters of each noise source on the ENC was taken into consideration. Since the energy resolution is affected by the electronic noise expressed by ENC, the obtained simulation results help to select the most feasible concept and parameters for a real system having a minimal noise effect. Using the program, several examples of the effect of those parameters on different ENC contributions related to the noise sources have been analyzed and discussed in detail. A comparison between the simulated results and experimental data was conducted. [ABSTRACT FROM AUTHOR]

Biosensing for diagnostics has risen rapidly in popularity over the past decades. With the discovery of new nanomaterials and morphologies, sensitivity is being constantly improved enough for reliable detection of trace biomarkers in human samples, like serum or sweat. This precision has enabled detailed research on the efficacy of biosensors. However, current biosensors suffer from reduced speed of operation. To make better use of this sensitivity, the development of a conductometric biosensor with in-situ use of an Laser Emitting Device (LED) display can provide rapid determination of sample results, steadily pushing biosensors toward more clinical, point-of-care (POC) applications. In this research, a simple LED was used for facile optical determination and visual output of an ultrasensitive bio-signal amplification circuit was made to interface with a B-type Natriuretic Peptide (BNP) biosensor. Tuning circuit gain enables an elegant method for adjustable separation of concentrations into 3 discrete categories: sub-threshold, analog, and saturation regions. These regions corresponded to 0 < [C] < 500 pg/mL (25, 100, 250 pg/mL, LED off), 500 < [C] < 1000 pg/mL (LED varying intensity), and 1000 pg/mL < [C] (LED full intensity). System efficacy was tested using human blood serum samples from University of Pittsburgh Medical Center patients, which were able to be accurately detected and sorted for rapid low cost and power. determination without need for complex digital elements. Additional specificity testing suggests insignificant impact of non-target biomarkers. [ABSTRACT FROM AUTHOR]

Phenotypic screens still produce novel drug discoveries, even though the emergence of the finer molecular details of cancer favors target-focused approaches. Recent efforts to improve the design and power of phenotypic screens have addressed the persistent shortcomings of target-focused discovery, such as those related to heterogeneity, resistance, or redundancy. Innovations in culturing patient-derived cancer cells in a tumor-relevant microenvironment have improved the disease relevance of findings. Technological advances for high-throughput screening, ranging from imaging to omics, have increased the information content of readouts and strengthened discovery by phenotype-focused approaches. Before molecular pathways in cancer were known to a depth that could predict targets, drug development relied on phenotypic screening, where the effectiveness of candidate chemicals is judged from functional readouts without considering the mechanisms of action. The unraveling of tumor-specific pathways has brought targets for molecularly driven drug discovery, but precedents in the field have shown that awareness of pathways does not necessarily predict therapeutic efficacy, and many cancers still lack druggable targets. Phenotypic screening therefore retains a niche in drug development where a targeted approach is not informative. We analyze the unique advantages of phenotypic screens, and how technological advances have improved their discovery power. Notable advances include the use of larger biological panels and refined protocols that address the disease-relevance and increase data content with imaging and omic approaches. [ABSTRACT FROM AUTHOR]

Presently, the combination of graph convolutional networks (GCN) with resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising approach for early diagnosis of autism spectrum disorder (ASD). However, the prevalent approach involves exclusively full-brain functional connectivity data for disease classification using GCN, while overlooking the prior information related to the functional connectivity of brain subnetworks associated with ASD. Therefore, in this study, the multiple functional connectivity–based graph convolutional network (MFC-GCN) framework is proposed, using not only full brain functional connectivity data but also the established functional connectivity data from networks of key brain subnetworks associated with ASD, and the GCN is adopted to acquire complementary feature information for the final classification task. Given the heterogeneity within the Autism Brain Imaging Data Exchange (ABIDE) dataset, a novel External Attention Network Readout (EANReadout) is introduced. This design enables the exploration of potential subject associations, effectively addressing the dataset's heterogeneity. Experiments were conducted on the ABIDE dataset using the proposed framework, involving 714 subjects, and the average accuracy of the framework was 70.31%. The experimental results show that the proposed EANReadout outperforms the best traditional readout layer and improves the average accuracy of the framework by 4.32%. [ABSTRACT FROM AUTHOR]

The 2020 decadal review recognized the measurement of the polarization of the cosmic microwave background (CMB) to be a top priority for the decade. CMB experiments including POLARBEAR2/Simons Array, Atacama Cosmology Telescope/Advanced-ACT, SPT-3G, the Simons Observatory, and CMB-S4 have or will use transition edge sensor (TES) bolometer fabricated with Aluminum doped with Manganese (AlMn). AlMn is a popular material choice as the superconducting transition temperature ( T c ) and normal resistance ( R n ) of the TES can be tuned with Mn concentration, geometric patterning, film thickness, and thermal annealing. In addition the conductivity is appropriate for both time division multiplexing and frequency division multiplexing that require 10 m Ω and 1 Ω sensors respectively. In this paper we present work on the ability to tune the T c of a film based on its time and temperature thermal tuning profile combined with room temperature monitoring of film resistivity. Such control allows for the fabrication of a wide range of TES parameters from a single AlMn concentration. Scanning electron microscope (SEM) imaging shows that the AlMn film's grain boundaries are changed by thermal annealing making the film more conductive and raising its superconducting transition temperatures, and that at high enough temperatures will eventually recover the T c of bulk Al. We find that baking films at ∼ 200 ∘ C for tens of minutes yields a T c that is suitable for 100 mK base temperature experiments and we present on the thermal tune profiles of several different thicknesses of AlMn. [ABSTRACT FROM AUTHOR]

The firmware developed for the readout and trigger processing of the information emerging from the BIS78-RPC Muon Spectrometer chambers in the ATLAS experiment at CERN is presented here, together with data processing techniques, data acquisition software, and tests of the readout chain system, which represent efforts to make these chambers operational in the ATLAS experiment. This work is performed in the context of the BIS78-RPC project, which deals with the pilot deployment of a new generation of sMDT+RPCs in the experiment. Such chambers are planned to be fully deployed in the whole barrel inner layer of the Muon Spectrometer during the Phase II upgrade of the ATLAS experiment. On-chamber front-ends include an amplifier, a discriminator ASIC, and an LVDS transmitter. The signal is digitized by CERN HPTDC chips and then processed by an FPGA, which is the heart of the readout and trigger processing, using various techniques. [ABSTRACT FROM AUTHOR]

The CMB-S4 experiment is developing next-generation ground-based microwave telescopes to observe the cosmic microwave background with unprecedented sensitivity. This will require an order of magnitude increase in the 100-mK detector count, which, in turn, increases the demands on the readout system. The CMB-S4 readout will use time-division multiplexing (TDM), taking advantage of faster switches and amplifiers in order to achieve an increased multiplexing factor. To facilitate the design of the new readout system, we have developed a model that predicts the bandwidth and noise performance of this circuitry and its interconnections. This is then used to set requirements on individual components in order to meet the performance necessary for the full system. We present an overview of this model and compare the model results to the performance of both legacy and prototype readout hardware. [ABSTRACT FROM AUTHOR]

Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the package is designed in a modular fashion which allows researchers to easily implement a range of different analyses. We also discuss how to format data to be able to use the package, and we give two examples of how to use the package to analyze real data. We believe that this package, combined with the rich data analysis ecosystem in R, will make it significantly easier for researchers to create reproducible decoding analyses, which should help increase the pace of neuroscience discoveries. [ABSTRACT FROM AUTHOR]

The images captured by a plane-array type infrared detector, formed by integrating a large-scale probe array and readout circuits via interconnection, is distorted by two types of superimposed nonuniform noises caused by the probe and readout circuitry. These nonuniform noises arise from the detector's constituent materials, fabrication processes, and other defects. In addition, the output response of an infrared detector is limited by the dynamic range of the circuit, the low and high ends of the output signal exhibit strong nonlinearities. To overcome the impact of detector response nonlinearity and achieve simultaneous correction for two different types of nonuniformities in infrared images, in this study, we proposed a novel correction method. First, the pixel points of all images are reordered according to their pixel values to provide a uniform reorganisation data. Then, the linear correlated region based on the recombined data is extracted using a judgment algorithm that processes the linear correlations of multiple regions to simulate the detector's response curve and find the nonuniformity correction coefficient of each pixel. Experiments on simulated noisy and real images show that our method accurately eliminates nonuniform IR noise without causing blurring and provides optimal peak signal-to-noise ratio and structural similarity.

Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the package is designed in a modular fashion which allows researchers to easily implement a range of different analyses. We also discuss how to format data to be able to use the package, and we give two examples of how to use the package to analyze real data. We believe that this package, combined with the rich data analysis ecosystem in R, will make it significantly easier for researchers to create reproducible decoding analyses, which should help increase the pace of neuroscience discoveries.

The firmware developed for the readout and trigger processing of the information emerging from the BIS78-RPC Muon Spectrometer chambers in the ATLAS experiment at CERN is presented here, together with data processing techniques, data acquisition software, and tests of the readout chain system, which represent efforts to make these chambers operational in the ATLAS experiment. This work is performed in the context of the BIS78-RPC project, which deals with the pilot deployment of a new generation of sMDT+RPCs in the experiment. Such chambers are planned to be fully deployed in the whole barrel inner layer of the Muon Spectrometer during the Phase II upgrade of the ATLAS experiment. On-chamber front-ends include an amplifier, a discriminator ASIC, and an LVDS transmitter. The signal is digitized by CERN HPTDC chips and then processed by an FPGA, which is the heart of the readout and trigger processing, using various techniques.

People routinely perform multiple visual judgments in the real world, yet, intermixing tasks or task variants during training can damage or even prevent learning. This paper explores why. We challenged theories of visual perceptual learning focused on plastic retuning of low-level retinotopic cortical representations by placing different task variants in different retinal locations, and tested theories of perceptual learning through reweighting (changes in readout) by varying task similarity. Discriminating different (but equivalent) and similar orientations in separate retinal locations interfered with learning, whereas training either with identical orientations or sufficiently different ones in different locations released rapid learning. This location crosstalk during learning renders it unlikely that the primary substrate of learning is retuning in early retinotopic visual areas; instead, learning likely involves reweighting from location-independent representations to a decision. We developed an Integrated Reweighting Theory (IRT), which has both V1-like location-specific representations and higher level (V4/IT or higher) location-invariant representations, and learns via reweighting the readout to decision, to predict the order of learning rates in different conditions. This model with suitable parameters successfully fit the behavioral data, as well as some microstructure of learning performance in a new trial-by-trial analysis.

The recording and analysis of biomagnetic fields have widespread applications in medical research and diagnostics. Wearable magnetic field sensors offer a noncontact and portable method for sensing biopotentials. This article presents a readout circuit in 180-nm CMOS for strain-modulated multiferroic vector magnetic field sensors. By utilizing a demodulator-first architecture, the circuit bandwidth and dynamic range requirements are greatly reduced allowing for a low power consumption of 5.9 mW. The circuit bandwidth is from 76 mHz to 2.2 kHz, allowing for measurement across the range of interest for biomagnetic signals. Utilizing a modulation noise cancellation technique, the noise performance of the sensor system is significantly improved, and the sensor modulation amplitude can be increased, resulting in improved sensor sensitivity. Measurements for the sensor-readout system demonstrate a 144 pT/ $\surd $ Hz magnetic noise floor at 1 kHz. The noise and power consumption are significantly lower than alternative magnetic sensor systems of similar volume.

Deep brain stimulation (DBS) devices capable of measuring differential local field potentials ( $\partial $ LFP) enable neural recordings alongside clinical therapy. Efforts to identify oscillatory correlates of various brain disorders, or disease readouts, are growing but must proceed carefully to ensure readouts are not distorted by brain environment. In this report we identified, characterized, and mitigated a major source of distortion in $\partial $ LFP that we introduce as mismatch compression (MC). Using in vivo, in silico, and in vitro models of MC, we showed that impedance mismatches in the two recording electrodes can yield incomplete rejection of stimulation artifact and subsequent gain compression that distorts oscillatory power. We then developed and validated an opensource mitigation pipeline that mitigates the distortions arising from MC. This work enables more reliable oscillatory readouts for adaptive DBS applications.

The nitrogen-vacancy (NV) center in diamond has been developed as a promising platform for quantum sensing, especially for magnetic field measurements in the nano-tesla range with a nano-meter resolution. Optical spin readout performance has a direct effect on the signal-to-noise ratio (SNR) of experiments. In this work, we introduce an online optimization method to customize the laser waveform for readout. Both simulations and experiments reveal that our new scheme optimizes the optically detected magnetic resonance in NV center. The SNR of optical spin readout has been witnessed a 44.1% increase in experiments. In addition, we applied the scheme to the Rabi oscillation experiment, which shows an improvement of 46.0% in contrast and a reduction of 12.1% in mean deviation compared to traditional constant laser power SNR optimization. This scheme is promising to improve sensitivities for a wide range of NV-based applications in the future. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) devices capable of measuring differential local field potentials ($\partial $ LFP) enable neural recordings alongside clinical therapy. Efforts to identify oscillatory correlates of various brain disorders, or disease readouts, are growing but must proceed carefully to ensure readouts are not distorted by brain environment. In this report we identified, characterized, and mitigated a major source of distortion in $\partial $ LFP that we introduce as mismatch compression (MC). Using in vivo, in silico, and in vitro models of MC, we showed that impedance mismatches in the two recording electrodes can yield incomplete rejection of stimulation artifact and subsequent gain compression that distorts oscillatory power. We then developed and validated an opensource mitigation pipeline that mitigates the distortions arising from MC. This work enables more reliable oscillatory readouts for adaptive DBS applications. [ABSTRACT FROM AUTHOR]

Field deployment is critical to developing numerous sensitive impedance transducers. Precise, cost-effective, and real-time readout units are being sought to interface these sensitive impedance transducers for various clinical or environmental applications. This paper presents a general readout method with a detailed design procedure for interfacing impedance transducers that generate small fractional changes in the impedance characteristics after detection. The emphasis of the design is obtaining a target response resolution considering the accuracy in real-time. An entire readout unit with amplification/filtering and real-time data acquisition and processing using a single microcontroller is proposed. Most important design parameters, such as the signal-to-noise ratio (SNR), common-mode-to-differential conversion, digitization configuration/speed, and the data processing method are discussed here. The studied process can be used as a general guideline to design custom readout units to interface with various developed transducers in the laboratory and verify the performance for field deployment and commercialization. A single frequency readout unit with a target 8-bit resolution to interface differentially placed transducers (e.g., bridge configuration) is designed and implemented. A single MCU is programmed for real-time data acquisition and sine fitting. The 8-bit resolution is achieved even at low SNR levels of roughly 7 dB by setting the component values and fitting algorithm parameters with the given methods. [ABSTRACT FROM AUTHOR]

In this work an innovative readout for multi-channel photodetectors is proposed, and the results of the prototype test are presented. This readout concept is based on the equalization and on the analog sum of a programmable number of channels. A configurable sum of individual channel signals allows for a reduction in the number of digitized channels, lowering the costs of future calorimeter granularity enhancement projects. A potential application of this new readout is in ATLAS-like hadron calorimeters. [ABSTRACT FROM AUTHOR]

MEMS based 3D double stacked tower pixel biosensor 10 × 10 array with integration of readout circuit for detection of saliva pH ion is demonstrated. The pixel biosensor comprised a driving electrode, sensing electrode and double stack tower pixel structure. The sensitivity of double stacked tower biosensor can be auxiliary enhanced by proposed lower-jitter low dropout regulator circuit and dual offset cancellation comparator. The double stacked tower sensor is fabricated by MEMS backend-of-line CMOS process, it is compatible with CMOS frontend readout circuits and integrated as a system-on-chip (SoC). The double stacked tower pixel by MEMS process is to obtain a larger volume ratio of charge groups in a pixel of biosensor to enhance the sensitivity and linearity for ion detection. With the double stacked tower structure in biosensor, the sensitivity is improved by 31% than that of single tower structure proved by simulation. A wide-range linearity from pH 2.0 to pH 8.3, high sensitivity of −21 ADC counts/pH (or 212 mV/pH), response time of 5 s, repetition of 98.9%, and drift over time of 0.5 mV are achieved. Furthermore, the proposed biosensor was performed to confirm the artificial saliva from healthy gingiva, chronic gingivitis and chronic periodontitis, the measured ADC counts from proposed biosensor SoC was in consistent of that measured cyclic voltametric (CV) method very well. The proposed 3D double stack tower biosensor and readout circuit can be further integrated with internet-of-thing (IoT) device and NFC for data transmission for continuous pH sensing to facilitate the chronic gingiva disease health care at home. [ABSTRACT FROM AUTHOR]

The biochemical processes in the body, including wound healing, are influenced by pH. When skin is damaged, in the inflammation phase, the pH of the skin is under pH 6.0, and in the granulation and spontaneous reepithelization phase, the pH of the skin oscillates between 6 and 8. If there is a frequent change in the wound dressing, it will make the damage to the skin hard to cure, especially for chronic wounds. In this article, we design and implement a highly sensitive micro-electro-mechanical-system (MEMS) cavity-based pH image biosensor of a $100\times100$ array with an integrated readout circuit in a CMOS bulk technology. Based on the proposed pH fringing electrical field model, we predict and optimize the performance of the proposed sensor pattern with cavity structure for operation frequency and various pH ions. The proposed pH biosensor with MEMS cavity structure exhibits a high sensitivity of 201 mV/pH, low drift voltage over time of 2.08 mV, a fast response time of 5 s, high precise repetition of 98.9%, and a pH image of 20 frame rate/s. Since the sensor is a $100\times100$ array, it displays a detailed change in the pH image of wound exudate to identify the inflammation stage under pH 6 or spontaneous reepithelization above pH 6 to indicate the need to change the dressing, reducing the clinician’s wasted resources on frequency change of wound dressings. [ABSTRACT FROM AUTHOR]

This document is an erratum for an article titled "NeuroDecodeR: a package for neural decoding in R." The erratum corrects a production error in the online version of the article, where the code phrase "cl_max_correlation" was incorrectly written as "cl: max_correlation" in several instances. The corrections have been made to the relevant sections of the article. The publisher apologizes for the mistake and has updated the original article. [Extracted from the article]

The High Luminosity era of the Large Hadron Collider (LHC) starting in 2029 promises exciting discovery potential, giving unprecedented sensitivity to key new physics models and precise characterization of the Higgs boson. In order to maintain current performance in this challenging environment, the ATLAS liquid argon electromagnetic calorimeter will get entirely new electronics that reads out the entire detector with full precision at the LHC frequency of 40 MHz, and provides high granularity trigger information, while withstanding high operational radiation doses. New results will be presented from both front-end and off-detector component development, along with highlights from machine learning applications. The future steps and outlook of the project will be discussed, with an eye towards installation in the ATLAS cavern beginning in 2026. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic remains a constant threat to human health, the economy, and social relations. Scientists around the world are constantly looking for new technological tools to deal with the pandemic. Such tools are the rapid virus detection tests, which are constantly evolving and optimizing. This paper presents a biosensor platform for the rapid detection of spike protein both in laboratory conditions and in swab samples from hospitalized patients. It is a continuation and improvement of our previous work and consists of a microcontroller-based readout circuit, which measures the capacitance change generated in an interdigitated electrode transducer by the presence either of sole spike protein or the presence of SARS-CoV-2 particles in swab samples. The circuit efficiency is calibrated by its correlation with the capacitance measurement of an LCR (inductance (L), capacitance (C), and resistance (R)) meter. The test result is made available in less than 2 min through the microcontroller's LCD (liquid-crystal display) screen, whereas at the same time, the collected data are sent wirelessly to a mobile application interface. The novelty of this research lies in the potential it offers for continuous and effective screening of SARS-CoV-2 patients, which is facilitated and enhanced, providing big data statistics of COVID-19 in terms of space and time. This device can be used by individuals for SARS-CoV-2 testing at home, by health professionals for patient monitoring, and by public health agencies for monitoring the spatio-temporal spread of the virus. [ABSTRACT FROM AUTHOR]

Neutrinoless double-beta decay (0 ν β β) is a key process to address some of the major outstanding issues in particle physics, such as the lepton number conservation and the Majorana nature of the neutrino. The next-generation of experiments aims at covering the Inverted-Ordering region of the neutrino mass spectrum, with sensitivities on the half-lives greater than 1 0 27 years. Among the exploited techniques, low-temperature calorimetry has proved to be a very promising one, and will keep its leading role in the future thanks to the CUPID experiment. CUPID will search for the neutrinoless double-beta decay of 100 Mo. By deploying Li 2 MoO 4 scintillating crystals enriched in 100 Mo and cryogenic light detectors, CUPID will perform simultaneous readout of heat and light signals, allowing for particle identification, and thus a powerful rejection technique for α background. To meet the new sensitivity target, the CUORE readout system must be upgraded. The readout system for each channel will consist of a pre-amplifier, a Programmable Gain Amplifier (PGA), a low-pass filter (Bessel filter) and a Digital AcQuisition System (DAQ). Finally, there is a pulser to generate thermal stabilization energy in the crystals. The motivation and expected performance of the technical upgrades are described in the context of the goals of the CUPID experiment. [ABSTRACT FROM AUTHOR]

The new ALICE Inner Tracking System (ITS2), installed in ALICE in May 2021 during a major upgrade campaign, significantly enhances the resolution on the determination of the distance of closest approach to the primary vertex, the tracking efficiency at low transverse momenta, and the read-out rate capabilities with respect to the previous tracker. It is equipped with silicon Monolithic Active Pixel Sensors (MAPS) with a pixel size of 27 × 29 μ m 2 , a spatial resolution of 5 μ m and it features a material budget of 0.36 % X 0 (inner layers). With a total sensitive area of about 10 m 2 , it represents the largest scale application of the MAPS technology in a HEP experiment and the first ever built at the LHC. This article shows the performance results of ITS2 during data taking in the LHC Run 3 where more than 250 billion events have been collected so far in proton–proton (pp) collisions at a center-of-mass energy of 13.6 TeV, five times more than Run 2. The detector is characterized by a stable spurious hit rate of about 10−8 hits/event/pixel and an impact parameter resolution of about 50 μ m at a transverse momentum of 500 MeV/ c. [ABSTRACT FROM AUTHOR]

Passive solid state dosimeters, such as thermoluminescence dosimeters, provide integrated measurement of the total dose and are widely used in environmental monitoring programs. The objective of this paper is to provide a comprehensive review on the use of thermoluminescent dosimetry methods for monitoring radiation dose in the environment. The article presents the part of the research results of the project PREPAREDNESS (EMPIR 2016 call for Metrology for Environment joint research project) with a particular objective to harmonize procedures used by dosimetry services, relevant authorities and Institutes across the Europe. To achieve this, different monitoring routines that are based on passive environ mental dosimetry methods are investigated. Differences in performing specific steps such as preheating, reading, annealing, minimizing fading, and others, are analyzed. The investigation was performed by means of qualitative literature review that showed the lack of information about specific steps. The conclusion of this work is that thermoluminescent dosimetry measurement system has to be type-tested even though the testing procedure is complicated. In addition to this, control dosimeters should be introduced, International Organization for Standardization protocols should be followed during calibration, and finally, parameters influencing the measurement uncertainty have to be identified and well understood in order to pro duce ac cu rate dose measurement results.

Objective: The objective of the paper is to present a novel systematic approach for the design and development of a portable, low-voltage low-power resistive sensor measurement system with high sensitivity. The goal of the proposed system is to be able to measure the $\Delta {R}/{R}_{s}$ for a wide range of baseline resistance of sensor ${R}_{s}$ ($1 {k}\Omega $ to $1 {M}\Omega $ i.e. 3 orders of magnitude) with high resolution and programmable sensitivity. Method: An auto-calibrated resistive sensor interface using a novel half-bridge based circuit is presented. The square-wave excitation and square-wave demodulation technique are utilised to reduce the power consumption of the proposed technique. Moreover, the sensitivity of the system is fully auto-calibrated for a wide range of base resistors. Results: With a supply voltage of 3.3 V and bandwidth of 10 Hz, the developed system is able to measure a minimum $\Delta {R}/{R}_{s}$ of about 20 ppm for $100 {k}\Omega $ base resistance. Overall, the power consumption of the developed system is 36.3 mW (for four channels). Significance: The system is useful for low-voltage low-power sensor nodes as a highly sensitive readout for the resistive sensor arrays. In addition, the developed system is useful for the stain sensors in the development of flexible hybrid electronics for wearable applications. [ABSTRACT FROM AUTHOR]

Field deployment is critical to developing numerous sensitive impedance transducers. Precise, cost-effective, and real-time readout units are being sought to interface these sensitive impedance transducers for various clinical or environmental applications. This paper presents a general readout method with a detailed design procedure for interfacing impedance transducers that generate small fractional changes in the impedance characteristics after detection. The emphasis of the design is obtaining a target response resolution considering the accuracy in real-time. An entire readout unit with amplification/filtering and real-time data acquisition and processing using a single microcontroller is proposed. Most important design parameters, such as the signal-to-noise ratio (SNR), common-mode-to-differential conversion, digitization configuration/speed, and the data processing method are discussed here. The studied process can be used as a general guideline to design custom readout units to interface with various developed transducers in the laboratory and verify the performance for field deployment and commercialization. A single frequency readout unit with a target 8-bit resolution to interface differentially placed transducers (e.g., bridge configuration) is designed and implemented. A single MCU is programmed for real-time data acquisition and sine fitting. The 8-bit resolution is achieved even at low SNR levels of roughly 7 dB by setting the component values and fitting algorithm parameters with the given methods.

This article presents a fully integrated Cryo-CMOS system on chip (SoC) for quantum computing. The proposed SoC integrates a radio frequency (RF) pulse modulator for qubit state manipulation, a multi-tone signal generator and a coherent receiver for qubit state readout, and 22 DACs for high-speed voltage pulsing of qubit gates. By adopting frequency division multiplexing and direct digital synthesis (DDS), the RF pulse modulator can control up to 16 qubits over a single RF line, and the readout receiver can detect the state of up to six qubits simultaneously. The proposed SoC also integrates a microcontroller for low latency on-chip signal processing and increased flexibility in implementing quantum instruction sets. A detailed analysis of qubit-state readout fidelity and the impact of finite DAC resolution on two-qubit gate fidelity is also included in this article, together with an electrical specification summary. The SoC is implemented in Intel’s 22 nm FFL fin field-effect transistor (FinFET) process, and it is characterized both at room and 4 K temperatures. The performance of each specific block is measured, with the readout characterized in a loop-back configuration. Generation of the control signals required for a full Rabi oscillation experiment is also demonstrated. This article also describes the cryogenic thermalization techniques used to integrate the SoC in the dilution refrigerator and shows temperature measurements during operation. [ABSTRACT FROM AUTHOR]

The new LHCb Vertex Locator (VELO) for LHCb, comprising a new pixel detector and readout electronics, will be installed in 2021 for data taking in Run 3 at the LHC. The electronics centers around the “VeloPix” ASIC at the front-end operating in a trigger-less readout at 40MHz. A custom serializer, called gigabit wireline transmitter (GWT), and associated custom protocol have been designed for the VeloPix. The GWT data are sent from the serializers of the VeloPix at a line rate of 5.12 Gb/s, reaching a total data rate of 2–3 Tb/s for the full VELO detector. Data are sent over 300-m optic-fiber links to the control and readout electronics cards for deserialization and processing in Intel Arria 10 FPGAs. Because of the VeloPix trigger-less design, latency variances up to 12 $\mu \text{s}$ can occur between adjacent datagrams. It is therefore essential to buffer and synchronize the data in firmware prior to onward propagation or suffer a huge CPU-processing penalty. This article will describe the architecture of the readout firmware in detail with focus given to the resynchronization mechanism and techniques for cauterization. Issues found during readout commissioning, and scaling resource utilization, along with the their solutions, will be illustrated. The latest results of the firmware data-processing chain can be presented as well as the verification procedures employed in simulation. Challenges for the next generation of the detector will also be presented with ideas for a readout processing solution. [ABSTRACT FROM AUTHOR]

MEMS based 3D double stacked tower pixel biosensor 10 × 10 array with integration of readout circuit for detection of saliva pH ion is demonstrated. The pixel biosensor comprised a driving electrode, sensing electrode and double stack tower pixel structure. The sensitivity of double stacked tower biosensor can be auxiliary enhanced by proposed lower-jitter low dropout regulator circuit and dual offset cancellation comparator. The double stacked tower sensor is fabricated by MEMS backend-of-line CMOS process, it is compatible with CMOS frontend readout circuits and integrated as a system-on-chip (SoC). The double stacked tower pixel by MEMS process is to obtain a larger volume ratio of charge groups in a pixel of biosensor to enhance the sensitivity and linearity for ion detection. With the double stacked tower structure in biosensor, the sensitivity is improved by 31% than that of single tower structure proved by simulation. A wide-range linearity from pH 2.0 to pH 8.3, high sensitivity of −21 ADC counts/pH (or 212 mV/pH), response time of 5 s, repetition of 98.9%, and drift over time of 0.5 mV are achieved. Furthermore, the proposed biosensor was performed to confirm the artificial saliva from healthy gingiva, chronic gingivitis and chronic periodontitis, the measured ADC counts from proposed biosensor SoC was in consistent of that measured cyclic voltametric (CV) method very well. The proposed 3D double stack tower biosensor and readout circuit can be further integrated with internet-of-thing (IoT) device and NFC for data transmission for continuous pH sensing to facilitate the chronic gingiva disease health care at home.

The readout in the dispersive regime is originally developed—and it is now largely exploited—for non‐demolitive measurement of super‐ and semiconducting qubits. More recently it has been successfully applied to probe collective spin excitations in ferro(i)magnetic bulk samples or collections of paramagnetic spin centers embedded into microwave cavities. The use of this readout technique within a semiclassical limit of excitation is only marginally investigated although it holds for a wide class of problems, including advanced magnetic resonance techniques. In this work, the coupling between a coplanar microwave resonator and diphenyl‐nitroxide organic radical diluted in a fully deuterated benzophenone single crystal is investigated. Two‐tone transmission spectroscopy experiments demonstrate the possibility to reconstruct the spectrum of the spin system with little loss of sensitivity with respect to the resonant regime. Likewise, pulse sequences of detuned microwave frequency allow the measurement of the spin‐lattice relaxation time (T1). The independent tunability of the probe and the drive power enables one to adjust the signal‐to‐noise ratio of the spectroscopy. These results suggest that electron spin dispersive spectroscopy can be used as a complementary tool of electron spin resonance to investigate the spin response. [ABSTRACT FROM AUTHOR]

An analog signal processing (ASP) circuit used for CMOS image sensor (CIS) readout is presented. The proposed ASP mainly includes a two-stage programmable gain amplifier (PGA), a sample-and-hold amplifier (SHA) merged by a pipelined analog–digital converter (ADC), and a digital-analog converter (DAC). Compared with conventional readout architecture, the proposed can provide finer gain, level shifting as well as offset calibrating function. A 1.5-Mpixel 60-fps CIS with the ASP is fabricated in a 0.13 μm 1P4M CMOS mixed signal process. The experiment results indicate the sensor can normally capture images without missing code. Moreover, the measured maximum gain error of the PGA is 1.6%, and the power dissipation is 16.5 mW for single ASP. [ABSTRACT FROM AUTHOR]

Surface roughness that is an inherent character of deposited films determines contact resistance at layer interfaces and thus may play a critical role in write and readout operations of phase-change electrical probe memory comprising multiple stacked layers. The surface roughness of different layered components was therefore simulated and their impact on resulting temperature, write/read current, and bit size was evaluated. It was revealed that using capping layer with larger root-mean-square and denser roughness significantly decreases the temperature and write/read current, and therefore forms a written bit embedded inside the active layer, while surface roughness of active layer, bottom electrode, and substrate have a negligible effect on electro-thermal characteristic and phase-transformation extent of the probe memory. To trigger optimum readout current and exempt from tip wear, probe tip was suggested to penetrate into the capping layer by 0.2 nm for the densest roughness of 3 nm root-mean-square.

The High Luminosity era of the Large Hadron Collider (LHC) starting in 2029 promises exciting discovery potential, giving unprecedented sensitivity to key new physics models and precise characterization of the Higgs boson. In order to maintain current performance in this challenging environment, the ATLAS liquid argon electromagnetic calorimeter will get entirely new electronics that reads out the entire detector with full precision at the LHC frequency of 40 MHz, and provides high granularity trigger information, while withstanding high operational radiation doses. New results will be presented from both front-end and off-detector component development, along with highlights from machine learning applications. The future steps and outlook of the project will be discussed, with an eye towards installation in the ATLAS cavern beginning in 2026.

The COVID-19 pandemic remains a constant threat to human health, the economy, and social relations. Scientists around the world are constantly looking for new technological tools to deal with the pandemic. Such tools are the rapid virus detection tests, which are constantly evolving and optimizing. This paper presents a biosensor platform for the rapid detection of spike protein both in laboratory conditions and in swab samples from hospitalized patients. It is a continuation and improvement of our previous work and consists of a microcontroller-based readout circuit, which measures the capacitance change generated in an interdigitated electrode transducer by the presence either of sole spike protein or the presence of SARS-CoV-2 particles in swab samples. The circuit efficiency is calibrated by its correlation with the capacitance measurement of an LCR (inductance (L), capacitance (C), and resistance (R)) meter. The test result is made available in less than 2 min through the microcontroller’s LCD (liquid-crystal display) screen, whereas at the same time, the collected data are sent wirelessly to a mobile application interface. The novelty of this research lies in the potential it offers for continuous and effective screening of SARS-CoV-2 patients, which is facilitated and enhanced, providing big data statistics of COVID-19 in terms of space and time. This device can be used by individuals for SARS-CoV-2 testing at home, by health professionals for patient monitoring, and by public health agencies for monitoring the spatio-temporal spread of the virus.

In this paper we present a $\mu $ -Coriolis mass flow sensor with differential capacitive readout, which can reduce the effect of common mode vibrations caused by external disturbances. The device consists of two silicon nitride micro-channel loops mechanically connected through two membranes. The sensor is actuated using Lorentz forces, inducing rotation of the sensor’s micro-channels. Coriolis forces of opposite sign are subsequently generated in the two channel loops. Thus, any common mode vibration can be distinguished from vibration induced by mass flow through the channels. Capacitive readout structures surround the sensor to detect these vibrations. A piezo-electric resonator is placed in close proximity to the sensor to generate artificial external disturbance. Flow measurements are executed to compare the differential readout method to a single ended readout with and without external disturbances. The flow sensitivity is unaffected. However, the mean deviation of the output signal is 60-70% lower when using the differential readout compared to single ended readout when external disturbances are induced. The effect of external disturbances on the zero flow stability is reduced by approximately 64% by using the differential readout. Full elimination of the effect is not possible due to non-linearity of the readout capacitors. Reducing non-linearity of the readout could further improve performance, e.g. by implementing (piezo-resistive) strain gauges on top of the micro-channel. [ABSTRACT FROM AUTHOR]

This paper presents a temperature-insensitive wideband cryogenic amplifier for superconducting nanowire single-photon detectors (SNSPD). With a proposed folded diode-connected transistor load to realize a good device-tracking feature, the theoretical derivations the simulations and test results prove that the amplifier-gain cell has a stable gain performance over a wide temperature range, solving the issues of a lack of the accurate cryogenic device models. The amplifier achieves a gain of 26 dB from 100 kHz to 1 GHz at 4.2 K, consuming only 1.8 mW from a 1.8 V supply. With a 0.13-μm SiGe BiCMOS process, the chip area is 0.5 mm².

This article proposes a new technique to reduce the readout noise in charge-coupled devices (CCDs). The objective is to minimize the common noise between the channels. This noise is either induced by the readout electronics or by the environment, and it could be present in small (single CCD and a few channels) or large systems (dozens of CCDs and hundreds of channels). By interleaving the video signals, we show that it is possible to reduce the readout noise by around 50% without sacrificing output channels at the expense of a moderate increase of the readout time, which is less compared with other techniques. Up until now, similar approaches have used half of the output stages only for noise measurement doubling the total readout time of the sensor. Simulations and experimental results are presented to validate the proposed approach. Moreover, the technique is measured using a Skipper CCD, and its operation is validated together with the ability of the Skipper sensor for nondestructively reading, several times, the same pixel charge. With the combination of both techniques, we achieve deep subelectron readout noise levels (0.1 e−). [ABSTRACT FROM AUTHOR]