Electrical Noise Research Articles

Electrical characteristics assessment and noise analysis of pocket-doped multi source T-shaped gate tunnel FET

A detailed analysis of a novel multi-source T-shaped gate Tunnel FET (MS-TTFET) is presented in this study in order to address the main issues with conventional TFETs. Due to its exceptionally low off-state current (IOFF) and remarkable sub-threshold properties, the tunnel field effect transistor has received a great deal of interest for low standby power applications. The MSR's integration into the SOI platform increases the device's on current, which increases the effective tunnelling area in the middle of the source-channel junctions. An isolator oxide has been added to prevent the source and drain regions from being directly coupled. A SiGe pocket has been suggested at the source and channel junction; silicon-germanium, a low bandgap material which enhances the tunnelling of charge carriers. A T-shaped gate can increase the effectiveness of the tunnel junction in order to limit the lower value of the leakage current. The multi-source regions of the T-shaped gate tunnel field effect transistor can boost the on-state current (ION) by improving tunnel junction areas. The device's ION/IOFF ratio, which is 1013 for our suggested construction, is the standard measure of merit for any device. It is possible to achieve an increased ON current of the order of 10−5 A/μm with a negligible subthreshold swing (SS) of 42 mV/decade. Moreover, the effect of low frequency flicker noise on the device behavior has been investigated.

Defect detection in the presence of geometrical artefacts

Defect detection in non-destructive testing relies on the ability to detect signals arising from defects and distinguish them from those arising from noise. Ultrasonic arrays are capable of producing multiple views of a component through combinations of mode conversions and reflections of waves from boundaries, which potentially contain additional information about defects. Noise in these views can come from random sources such as electrical noise and thermal fluctuation, and coherent sources including stochastic microstructural noise, and boundary reflections giving rise to geometrical artefacts. The number of geometrical artefacts increases with both the geometrical complexity of the part under test, and with the number of reflections used to construct a view. This paper tackles the challenge of defect detection in the presence of geometrical artefacts by characterising the intensity distribution at each point in every view produced within a pristine sample, and using a hypothesis test as a means of defect detection. This information is combined with state-of-the-art data fusion routines, improving detection performance. Experimental work shows that this method improves performance compared to other methods of defect detection over the same scan area, which assume that the defect does not occupy the same location as an artefact in at least one view. Provided the pristine sample is characterised sufficiently well, hypothesis testing provides a robust method of defect detection in the presence of geometrical artefacts.

Effect of 60Coγ ray radiation on electrical properties of SiGe HBTs at low temperatures

In this article, total ionizing dose (TID) irradiation experiments were carried out on germanium-silicon heterojunction bipolar transistors (SiGe HBTs) under different bias and temperature conditions. The electrical performance and noise characteristics of the device were characterized before and after irradiation using a semiconductor parameter analyzer and a 1/f noise test system, respectively. The variation of radiation sensitivity parameters with the TID effect and temperature of the device was obtained, and the mechanism of radiation damage degradation was also identified. Results suggest that the oxide-trap charges and interface states induced by irradiation are the main causes for SiGe HBT electrical parameters degradation. With the decreased temperature, Gummel characteristics and 1/f noise are significantly improved. It's found that oxide-trap charges and interface states generated at 93 K is less than that at 300 K. This work lays the foundation for the application of SiGe HBT in aerospace area.

Continuously monitoring of muscle fatigue based on a wearable micromachined ultrasonic transducer probe

Ultrasound based methods can assess muscle fatigue by penetrating deep muscles and are not susceptible to electrical noise, but traditional ultrasound probes have bulky size that hinders integration and wearability, making them impractical to continuously monitor muscle status. This paper proposed a wearable probe based on piezoelectric micromachined ultrasonic transducers (PMUTs), where a polymer acoustic lens was incorporated to focus ultrasonic beam and enhance the emitted acoustic pressure (increased by >20%). The fatigue dynamics of upper arm muscles during sustained exertion was investigated by continuously monitoring thickness changes with the PMUTs-based probe, revealing a temporal pattern characterized by swift and subsequent gradual thickening. Several muscle fatigue metrics were derived by piecewise linear fitting, including the initial and secondary deformation rate and the transition time, offering insight into muscle status and fatigue resistance. Additionally, an outlier elimination method for thickness profiles was utilized, mitigating the influence of uncontrollable motion on findings. Leveraging PMUTs technology and acoustic lens integration, this proposed wearable approach holds promise for real-time assessment of muscle fatigue.

Controlled etching of MXene for highly selective triethylamine detection at room temperature

MXene, exemplified by Ti3C2Tx, was obtained as a viable candidate for sensing gases at room temperature. However, in the current study, increased power consumption and poor process reproducibility resulted from the long cycle times to prepare MXene and MXene-based gas-sensitive materials. In this study, a six-sided high-intensity ultrasonic wave was applied to the etching environment, and a series of MAX-MXene heterostructures were effectively obtained in one step by adjusting the etchant content. Due to the unique structure of MXene flake and the generation of endogenous MAX-MXene heterostructure, the MAM gas sensor exhibits a response value of 4.1% to 10 ppm TEA at room temperature (23 °C) and relative humidity of 84%, providing fast response recovery time, low electric noise and excellent selectivity, with a comprehensive gas sensing performance superior to that of the pristine MXene materials reported before. This study provides new ideas for designing and applying new substrate materials based on MAX and MXene.

Adaptive analog and digital active noise control

Digital adaptive controllers are widely used for active noise control. The secondary path delay, including the anti-aliasing, sampling, and reconstruction effects, must be shorter than that in the primary path to maintain good broadband performance. To eliminate the added delay of the sampling, a mixed analog and digital adaptive feedforward controller is developed. The analog controller is based on a state-filtered adaptive linear combiner, while the digital one uses an adaptive finite-impulse-response filter. The adaptation of the analog controller is derived based on a sampled version of the normalized projection algorithm, while the digital controller adaptation is based on the filtered-reference least-mean-squares algorithm. Two configurations are considered for the analog combiner. One aims to minimize the electrical noise by introducing individual state filters, all driven by the same reference signal. The other gives a superior bandwidth by driving each component filter with the previous filter output. The performance of the proposed controller is assessed and compared with separate analog or digital controllers. The results highlight that the suggested controller obtains significant attenuation levels close to the causality limit using the analog controller but also allows long impulse responses using the sampled controller.

Van der Waals heterostructure gas sensors based on narrow–wide bandgap semiconductors for superior sensitivity

Achieving high sensitivity in gas sensors is crucial for the precise detection of toxic agents. However, this can be challenging as it requires gas sensors to possess both a high response signal and low electrical noise simultaneously, which seems controversial as it necessitates adopting semiconductors with different bandgaps. Herein, we demonstrate the superior sensitivity of 2D molybdenum disulfide (MoS2)/tellurium (Te) van der Waals heterostructure (vdWH) gas sensors fabricated by combining narrow-bandgap (Te) and wide-bandgap (MoS2) semiconductors. The as-fabricated MoS2/Te vdWH gas sensors exhibit excellent sensitivity that is unavailable for sensors based on its individual counterparts. The response toward 50 ppm NH3 is improved by two and six times compared to the individual MoS2 and Te gas sensors, respectively. In addition, a high signal-to-noise ratio of ∼350 and an ultralow limit of detection of ∼2 ppb are achieved. These results outperform most previously reported gas sensors due to the efficient modulation of the barrier height of the MoS2/Te p–n junction as well as the synergistic effect benefiting from the low electric noise of the narrow-bandgap Te and high response signal of the wide-bandgap MoS2. Our work provides an insight into utilizing vdWHs based on narrow–wide bandgap semiconductors for developing highly sensitive gas sensors.

Capacitive sensing for measuring the conduction zone of low density pentaerythritol tetranitrate (PETN)

We present a novel diagnostic system, based on the principle of capacitive sensing, capable of detecting the conduction zone of detonating explosives. The method relies on measuring the change in the capacitive coupling between a printed circuit and its surroundings, induced by an increase in local conductivity. A high frequency wave generator provides the driving signal, which is modified by the change in coupling. This method boasts greater sensitivity than existing conductivity-based techniques, with comparable spatial and temporal resolution. The subject material was pentaerythritol tetranitrate (PETN) powder, pressed into low density (50%–85% theoretical maximum density) cylindrical columns. Previous attempts to measure the reaction zone of low density PETN have faced difficulties because of its short length and low conductivity compared to other explosives. The PETN was initiated using a laser flyer detonator in order to reach detonation promptly and with a minimum of electrical noise. Application of the capacitive sensor diagnostic and subsequent analysis yielded a density dependent steady-state reaction zone length of between 45 and 78 μm.

CNSC-22. CANCER NEUROSCIENCE IN THE OR: ELECTROPHYSIOLOGIC INSIGHTS AT THE TIME OF BRAIN TUMOR RESECTION

Abstract Pioneering works by Venkatesh and Venkataramani demonstrated that formation of functional neural-glial synapses fosters development and growth of glial neoplasms. Resultant peritumoral neural hyperexcitability associates with tumor progression, though limited in vivo human data have been evaluated to date. We sought to enrich our understanding of these mechanisms by obtaining thin film intraoperative micro-electrocorticography (µECoG) recordings from patients undergoing primary brain tumor resection. METHODS: Intraoperative µECoG data for 8 patients with low- (LGG) and high-grade glioma (HGG, WHO grades 3 and 4) of various tumor histology were included. Analyses excluded any operative behavioral or cognitive tasks and evaluated data prior to stimulation for language or motor mapping. Following low pass (< 200 Hz) and notch filtering at 60, 120, and 180 Hz for ambient electrical noise, bandpower was calculated using multitaper spectral analysis for low frequency (LF, < 1 Hz), delta (1-4 Hz), beta (12-30 Hz), gamma (30-80 Hz), and high gamma (80-200 Hz) bands. RESULTS: Compared to isocitrate dehydrogenase (IDH) wild-type tumors, those with IDH-mutation (n = 3; two WHO grade 3, one grade 4) demonstrated the highest proportion of power within the LF band (92.2% vs 71.4%) with relatively lower proportional beta (3.6% vs 18.3%) and gamma (2.2% vs 8.7%) bandpower. The same pattern was seen in HGG (n = 5) vs LGG (n = 3) tumors with higher LF (84.5% vs 70.3%) and lower beta (10.3% vs 16.8%) and gamma (3.5% vs 10.8%) bandpower, perhaps driven by three HGGs with IDH mutation. Absolute bandpower was highest at all bands in a patient with left frontal glioblastoma. CONCLUSIONS: Interesting patterns are emerging that corroborate differential high and low frequency neural activity among patients with primary brain tumors. Disparities in LF and beta/gamma bands may represent therapeutic targets or a non-invasive biomarker of disease activity but require further, ongoing data collection and analyses.

Binding Mechanism and Surface-Enhanced Raman Scattering of the Antimicrobial Sulfathiazole on Gold Nanoparticles.

A combined study using the surface-enhanced Raman scattering (SERS) technique and quantum chemical calculations was carried out to elucidate the adsorption behavior of sulfathiazole, an antibiotic drug, on gold nanoparticles. The tetrahedral Au20 cluster was used as a simple model to mimic a nanostructured gold surface. Computations using density functional theory with the PBE functional were performed in both the gas phase and aqueous medium using a continuum model. The drug is found to bind to the Au metals via the nitrogen of the thiazole ring. The interaction is also partially stabilized by the ring-surface π coupling rather than a sideway adsorption as previously proposed. In an aqueous solution, the drug molecule mainly exists as a deprotonated form, which gives rise to a much greater affinity toward Au nanoparticles as compared to the neutral forms. The drug adsorption further induces a significant alteration on the energy gap of the gold cluster Aun, which could result in an electrical noise. Notable SERS signals below 1600 cm-1, which result from a coupling of several vibrations including the ring breathing, C-C stretching, and N-H bending, could be employed for both qualitative and quantitative detection and assessment of sulfathiazole at trace concentrations.

Validation of Electrical Noise of a DC Motor through Controlled Varistor Cracking: An Experimental Study

The varistor is an electronic component that protects the DC motor’s circuitry from electrical noise or transients that can cause damage. It works as a voltage-dependent resistor that can change its resistance according to the applied voltage. Once the voltage surpasses a specific threshold, the varistor conducts and directs the excess voltage away from the motor’s circuitry. In small DC motor manufacturing, ring varistors are vital for reducing electrical noise, minimizing spark-induced damage to the commutator and brush, and extending the motor’s lifespan. Additionally, they prevent damage to electronic parts in the customer’s mechanism set. The objective of this study is to investigate the impact of varistor cracks or chips that may occur during the soldering process of varistors to the commutator. To confirm the occurrence of cracks or chips, intentional damage will be inflicted on the varistors. The study aims to determine how the presence of cracked or chipped varistors affects the electrical noise produced by a DC motor during its operation. The resulting spark was observed through an oscilloscope, and it was found that the effect could be substantial, up to 5 to 10 times the rated voltage supplied to the motor. In the next phase of this study, further tests will be conducted on motors without varistors to provide a comparison.

Effects of exposure to alternating low-intensity, intermediate-frequency electric fields on the differentiation of human leukemic cell line U937.

Studying the bioeffects of electric fields have been the subject of ongoing research which led to promising therapeutic effect, particularly in cancer treatment. Here, we investigated the impact of low-intensity, intermediate-frequency alternating electric fields on the differentiation of human myeloid leukemia cell line U937. The results showed a near twofold increase in differentiation of U937 cells treated for 24 h by alternating 600 kHz, 150 V/m electric fields, in comparison to their control groups. This measure was evaluated by latex bead phagocytosis assay, nitro blue tetrazolium test, and cell cycle analysis which revealed a significant shift in the number of cells from G2 +M to G0 +G1 phases. The simulation result for the intracellular field intensity showed around 50% attenuation with respect to the applied external field for our setup which ruled out masking of the applied field by the internal electric noise of the cell. Based on previous studies we postulate a possible calcium-related effect for the observed differentiation, yet the exact underlying mechanism requires further investigation. Finally, our results may offer a potential therapeutic method for leukemia in the future.

Machine learning predictive model for motor moise by expanding dataset using electromagnetic force characteristics

To understand the variations of electric vehicle motor noise, the electromagnetic force change due to the tilting and the eccentricity between the rotor and the stator on the noise should be studied. However, it takes enormous computation time to consider all the various conditions, so machine learning (ML) model that can reduce the analysis time of electromagnetic force and noise was developed. The developed ML model showed very high accuracy prediction performance with an R2 value higher than 0.99 even for data that was not used at all for training. The speed increase was more than 100 times of FE analysis. The authors doubt that the high accuracy of the developed model implies the potential for electromagnetic force data structure to be much simpler than it appears. Therefore, various analyzes were conducted on the force and uneven magnetic pull. The change in the temporal-spatial main frequency component was not large, and only the change in the side order was meaningful. It was confirmed that to create a predictive model using a smaller dataset for not only the UMP but also all the individual teeth force is possible. This new way of creating a training dataset for an ML model helps to increase the training efficiency. Based on the generated prediction model, it can be used to identify changes in noise characteristics in major rotational orders and derive suitable improvements.

Impact of RF stress on different topologies of 100 nm X-band robust GaN LNA

In this article, we study the robustness of 3 versions of a single stage LNA configured according to different modes of detectivity or robustness against electromagnetic jamming signals. Four successive sequences of RF step stress at 10 GHz are applied to each of the 3 LNAs under study. These robust MMIC LNAs have been designed using the D01GH GaN process from OMMIC technology, to switch from a nominal low noise mode to a high linearity mode. This DC-bias switch allows increasing the power input 1 dB compression point by 8 dB. This study focuses on the robustness of these LNAs (LNA#A for agile) when they operate under nominal low-noise mode (featuring lower IP1dB) or under nominal high-linearity mode (at the price of a degraded noise figure NF50). This original LNA#A is compared to a robust conventional design using a larger sized device (LNA#R for robust). The step-stresses are operated at 10 GHz, which is the center frequency band of these LNAs. All modes of operation are shown to exhibit fairly reproducible step stress plots, although thermal or nonlinear effects can be differentiated between low-noise and high-linearity operating conditions, and compared with the robust design LNA#R. We demonstrate the relevance of an alternative approach to conventional LNA circuit design strategies in order to achieve natural electronic protection, without a limiter placed before the LNA#A or LNA#R or without turning-off the DC-biasing: this protection option benefits from maintaining the LNA in an operational detection situation for longer when the incident input signal increases, without any degradation in electrical performance (DC and RF) or noise (NF50), even after many sequences of RF step stress.

Origin of electrical noise near charge neutrality in dual gated graphene device

This Letter investigates low frequency 1/f noise in an hBN encapsulated graphene device in a dual gated geometry. The noise study is performed as a function of top gate carrier density (nTG) at different back gate density (nBG). The noise at low nBG is found to be independent of top gate carrier density. With increasing nBG, noise value increases, and a noise peak is observed near charge inhomogeneity of the device. A further increase in nBG leads to a decrease in noise magnitude. The shape of the noise is found to be closely related to a charge inhomogeneity region of the device. Moreover, the noise and conductivity data near charge neutrality show clear evidence of noise emanating from a combination of charge number and mobility fluctuation.

Small samples noise prediction of train electric traction system fan based on a multiple regression-fuzzy neural network

The fan noise in the train electric traction system contributes to the main noise of the train, affecting the comfort of the passengers. The prediction of fan noise can serve as a foundation for the design and fault diagnosis of the fan in the train electric traction system as well as noise reduction. This paper proposes a prediction model based on multiple regression analysis and a double-layer fuzzy neural network (MRA-2LFNN). After the multiple regression analysis of the train electric traction system fan noise is performed to determine the prediction input, a fuzzy neural network is used to predict the fan noise. Afterward, further analysis of noise influencing factors is presented based on the experimental results, and the possibility of the fan optimal design is discussed. This method can make accurate predictions using a small sample of fan noise data, reducing the need for data samples and lowering costs. The experimental results show that the proposed method can achieve better prediction results using the small sample dataset by training and verifying the actual train electric traction system fan noise data, and the average prediction accuracy rate is 94.15%, After discussion, when the number of fan blades is 16, the train electric traction system exhibits improved noise performance.

Modified Neuropixels probes for recording human neurophysiology in the operating room.

Neuropixels are silicon-based electrophysiology-recording probes with high channel count and recording-site density. These probes offer a turnkey platform for measuring neural activity with single-cell resolution and at a scale that is beyond the capabilities of current clinically approved devices. Our team demonstrated the first-in-human use of these probes during resection surgery for epilepsy or tumors and deep brain stimulation electrode placement in patients with Parkinson's disease. Here, we provide a better understanding of the capabilities and challenges of using Neuropixels as a research tool to study human neurophysiology, with the hope that this information may inform future efforts toward regulatory approval of Neuropixels probes as research devices. In perioperative procedures, the major concerns are the initial sterility of the device, maintaining a sterile field during surgery, having multiple referencing and grounding schemes available to de-noise recordings (if necessary), protecting the silicon probe from accidental contact before insertion and obtaining high-quality action potential and local field potential recordings. The research team ensures that the device is fully operational while coordinating with the surgical team to remove sources of electrical noise that could otherwise substantially affect the signals recorded by the sensitive hardware. Prior preparation using the equipment and training in human clinical research and working in operating rooms maximize effective communication within and between the teams, ensuring high recording quality and minimizing the time added to the surgery. The perioperative procedure requires ~4 h, and the entire protocol requires multiple weeks.

Upgrade of the MARI spectrometer at ISIS

The MARI direct geometry time-of-flight neutron spectrometer at ISIS has been upgraded with an m=3 supermirror guide and new detector electronics. This has resulted in a flux gain of ≈6× at λ=1.8 Å, and improvements on discriminating electrical noise, allowing MARI to continue to deliver a high quality science program well into its fourth decade of life.

Efficient employment of Yb doped multi-mode fiber amplifiers based pumped laser with advanced light sources in high speed modulated optical communication systems

Abstract In this work, efficient employment of Yb doped multi-mode fiber amplifiers based pumped laser is simulated with advanced light sources in high speed modulated optical communication systems. The total lighted power through measured index multimode fiber based different base band modulation voltages dual drive MZM measured are clarified. The 3D graph of the mode field intensity distribution through measure index multimode fiber is simulated. Total electrical base ban power form through PIN photo-detector based various modulation voltages Dual drive MZM measured are demonstrated. The max Q form factor-min base band BER values through PIN photo-detector based different modulation voltages dual drive MZM measured are also simulated. The electrical base signal form noise ratio varies is simulated and clarified with modulation voltage based dual drive MZM measure varies. The max Q form base band Factor is demonstrated with modulation voltage based dual drive MZM measure varies. Total lighted base power form varies are clarified with modulation voltage based dual drive MZM measure varies. The different measured index multimode fiber characteristics based spatial VCSEL are also clarified and demonstrated.

Enhanced performance of on-chip integrated biosensor using deep learning

A new approach for determining the concentration composition of a multi-element media using a micro-ring resonator (MRR) is proposed which allows for both electrical and thermal noise removal as well as moderately higher average accuracy. This method uses two neural networks, namely a convolutional neural network (CNN) and a deep neural network (DNN). The CNN differentiates the transmission spectrum from the noise. This spectrum is used to obtain selected features before being fed into the DNN, which determines the concentration of each chemical in the analyte. Both models are trained to work using simulated data from a silicon on-insulator ring resonator operating between the infrared wavelengths of lambda =1.46,upmu hbox{m} to lambda =1.6,upmu hbox{m} on mixtures of water, ethanol, methanol, and propanol, although the same approach can be used with other designs and substances. The CNN was trained using the MRR transmission spectra superimposed with white Gaussian noise as well as Poisson noise to mimic different noise sources, while the DNN underwent training on the extracted features. Average root-mean-square error for element concentration for the entire system is 0.0775% for a range of concentrations from 0.0357 to 75%, and the largest error had a value of 0.68% concentration.