Gate Modulation Research Articles

Impacts of Cavity Thickness and Insulating Material on Dielectric Modulated Trench Junction-less Double Gate Field Effect Transistor for Biosensing Applications

Introduction: This work represents the influence of gate dielectric, and the nano-cavity gap of a dielectric modulated trench gate Junction-less Double Gate Field Effect Transistor (JL-DGFET) on the different performance indicators is investigated considering the Low-Frequency Noise. Methods:: It is noted that the gate dielectric and the nanogap, both parameters, have a substantial influence on the sensing capacity and performance of noise of the device. Results:: A double gate suitable dielectric material and cavity thickness can effectively improve the biosensor’s sensitivity with a minimum amount of noise. Conclusion:: The sensitivity is found to increase up to 9.5 for dielectric constant, k = 3.57 and 6.5 for dielectric constant, k = 2.1.

Oriented triplex DNA as a synthetic receptor for transmembrane signal transduction.

Signal transduction across biological membranes enables cells to detect and respond to diverse chemical or physical signals, and replicating these complex biological processes through synthetic methods is of significant interest in synthetic biology. Here we present an artificial signal transduction system using oriented cholesterol-tagged triplex DNA (TD) as synthetic receptors to transmit and amplify signals across lipid bilayer membranes through H+-mediated TD conformational transitions from duplex to triplex. An auxiliary sequence, complementary to the third strand of the TD, ensures a controlled and preferred outward orientation of cholesterol-tagged TD on membranes. Upon external H+ stimuli, the conformational change triggers the translocation of the third strand from the outer to the inner membrane leaflet, resulting in effective transmembrane signal transduction. This mechanism enables fluorescence resonance energy transfer (FRET), selective photocleavage, catalytic signal amplification, and logic gate modulation within vesicles. Our findings demonstrate that these TD-based receptors mimic the functional dynamics of natural G protein-coupled receptors (GPCRs), providing a foundation for advanced applications in biosensing, cell signaling modulation, and targeted drug delivery systems.

ChemXTree: A Feature-Enhanced Graph Neural Network-Neural Decision Tree Framework for ADMET Prediction.

The rapid progression of machine learning, especially deep learning (DL), has catalyzed a new era in drug discovery, introducing innovative approaches for predicting molecular properties. Despite the many methods available for feature representation, efficiently utilizing rich, high-dimensional information remains a significant challenge. Our work introduces ChemXTree, a novel graph-based model that integrates a Gate Modulation Feature Unit (GMFU) and neural decision tree (NDT) in the output layer to address this challenge. Extensive evaluations on benchmark data sets, including MoleculeNet and eight additional drug databases, have demonstrated ChemXTree's superior performance, surpassing or matching the current state-of-the-art models. Visualization techniques clearly demonstrate that ChemXTree significantly improves the separation between substrates and nonsubstrates in the latent space. In summary, ChemXTree demonstrates a promising approach for integrating advanced feature extraction with neural decision trees, offering significant improvements in predictive accuracy for drug discovery tasks and opening new avenues for optimizing molecular properties.

Optical gating characteristic time of measurement with 20 picoseconds resolution for an ultrafast image intensifier

In order to study the optical gating time characteristic of an ultra-fast gated image intensifier, a precision measuring system with 20 ps (picoseconds) trigger jitter is established. The sequential gate images of the image intensifier with a time interval of 20 ps are obtained at 0.125 ns ∼2 ns (nanoseconds) gating electrical pulse modes. These images show that the intensifier has the familiar normal “iris” turn-on and abnormal “iris” turn-off behavior. The turn- on time is about 130 ps and the turn-off time is about 170 ps. In 1 ns and 2 ns gating modes, the fully-on time of optical gating almost is equal to the width of the electrical gating pulse, In 0.5 ns, 0.25 ns and 0.125 ns modes, the fully-on time is less than the width of the electrical gating pulse, especially in the 0.125 ns gating mode, and the image in fully-on phase is not observed. The width at 90% of the peak of the optical gating time response curve may better characterize the fully-on time of optical gating.

Low‐temperature SOI SiGe/Si superlattice FinFET with omega‐shaped channel and self‐allied silicide for 3D sequential IC

AbstractIn this letter, to improve the performance and reduce leakage currents of bulk low‐temperature multi‐layer SiGe/Si superlattice (SL) fin field‐effect transistors (FinFETs), a p‐type omega‐shaped channel (Ω‐channel) SL FinFET is realized by etching a Si/Si0.7Ge0.3 triple‐layer stacked structure in a replacement metal gate (RMG) module on a silicon‐on‐insulator (SOI) substrate. In addition, a self‐allied Ni0.9Pt0.1 silicide process and a low‐thermal‐budget (≤400°C) integration procedure were performed on the Ω‐channel SL FinFET. Test results demonstrate that the on‐state current (Ion) is increased by 5.5 times (from 78 to 429 µA/µm) and the off‐state current (Ioff) is reduced by 78.8% (from 5.2 × 10−3 to 1.1 × 10−3 µA/µm) when compared with the corresponding currents of traditional bulk SL FinFETs.

Модель та метод навчання детектора об’єктів на аерозображенні з оптимізацією робастності та кількості обчислень

The subject of research is Neural network-based object detectors, which are widely used for video image analysis. An increasing number of tasks now demand data processing directly at the source, which limits the available computational resources. However, the vulnerability of neural networks to noise, adversarial attacks, and weight error injections significantly diminishes their robustness and overall effectiveness. The relevant task is to develop models that provide both computational efficiency and robustness against perturbations. This paper investigates a model and method for enhancing the robustness of neural network detectors under limited resources. The objective is to design a model that allocates resources optimally while maintaining stability. To achieve this, the study employs techniques such as dynamic neural networks, robustness optimization, and resilience strategies. The following results were obtained. A detector with a feature extractor based on ViT-S/16, modified with gate modules for dynamic examination was developed. The model was trained on the RSOD dataset and meta-learned on the adaptation results to various perturbations. The model's resistance to random bit inversions in weights (10 % of weights) and to adversarial attacks with perturbation amplitudes up to 3/255 (L∞ norm) was tested. Conclusion. The proposed detector model incorporating dynamic examination and optimized robustness, reduced floating-point operations by over 20 % without loss of accuracy. A novel method for training the detector was developed, combining the RetinaNet loss function with the loss function of gate blocks and applying meta-learning on the adaptation results for various types of synthetic perturbations. Testing demonstrated an increase in accuracy by 11.9 % under the influence of error injection and by 13.2 % under the influence of adversarial attacks.

Ferroelectric Polarization Enhanced Optoelectronic Synaptic Response of a CuInP2S6 Transistor Structure.

Neuromorphic computing can simulate brain function and is a pivotal element in next-generation computing, providing a potential solution to the limitations brought by the von Neumann bottleneck. Optoelectronic synaptic devices are highly promising tools for simulating biomimetic nervous systems. In this study, we developed an optoelectronic neuromorphic device with a transistor structure constructed using ferroelectric CuInP2S6. Essential synaptic behaviors in this device are observed in response to light and electrical stimuli. The optoferroelectric coupling is revealed, and the highly tunable gate modulation of the charge carrier is realized in a single device. On this basis, the light adaptation of the biological eyes and smarter Pavlovian dogs was implemented successfully and enhanced by ferroelectric polarization. The gate voltage application promotes the migration of additional Cu+ ions in the in-plane direction, thus enhancing the synaptic performance of electrical stimulation. Meanwhile, the processing ability of convolutional kernel noise images in ferroelectric devices has been achieved. Our results offer the important observation and application of ferroelectric polarization-enhanced synaptic properties of a transistor structure and have great potential in promoting the development of two-dimensional van der Waals materials and devices.

Symmetry-adapted Markov state models of closing, opening, and desensitizing in α 7 nicotinic acetylcholine receptors.

α7 nicotinic acetylcholine receptors (nAChRs) are homopentameric ligand-gated ion channels with critical roles in the nervous system. Recent studies have resolved and functionally annotated closed, open, and desensitized states of these receptors, providing insight into ion permeation and lipid binding. However, the process by which α7 nAChRs transition between states remains unclear. To understand gating and lipid modulation, we generated two ensembles of molecular dynamics simulations of apo α7 nAChRs, with or without cholesterol. Using symmetry-adapted Markov state modeling, we developed a five-state gating model. Free energies recapitulated functional behavior, with the closed state dominating in absence of agonist. Open-to-nonconducting transition rates corresponded to experimental open durations. Cholesterol relatively stabilized the desensitized state, and reduced open-desensitized barriers. These results establish plausible asymmetric transition pathways between states, define lipid modulation effects on the α7 nAChR conformational cycle, and provide an ensemble of structural models applicable to rational design of lipidic pharmaceuticals.

Ammonia gas sensors based on multi-wall carbon nanofiber field effect transistors by using gate modulation

Ammonia gas sensors play a critical role in environmental monitoring and healthcare applications due to the harmful effects of ammonia exposure. In this study, I present a novel approach to ammonia detection using field-effect transistors (FETs) based on Multi-Wall Carbon Nanofibers (MWCNFs), known for their outstanding electrical properties. I fabricate and characterize MWCNF-FET sensors, utilizing interdigitated Source-Drain electrodes directly deposited onto aligned semiconducting MWCNFs. By systematically investigating the effect of gate bias on sensor performance, I demonstrate that applying a positive gate voltage enhances the sensor's gas response by up to 55 % across varying concentrations of ammonia. Additionally, I show that appropriate gate modulation significantly improves the device's reversibility, ensuring reliable, repeatable measurements. These findings highlight the potential of MWCNF-FETs in developing high-performance, gate-tunable ammonia gas sensors for real-world applications.

TRPML1 gating modulation by allosteric mutations and lipids

Transient Receptor Potential Mucolipin 1 (TRPML1) is a lysosomal cation channel whose loss-of-function mutations directly cause the lysosomal storage disorder mucolipidosis type IV (MLIV). TRPML1 can be allosterically regulated by various ligands including natural lipids and small synthetic molecules and the channel undergoes a global movement propagated from ligand-induced local conformational changes upon activation. In this study, we identified a functionally critical residue, Tyr404, at the C-terminus of the S4 helix, whose mutations to tryptophan and alanine yield gain- and loss-of-function channels, respectively. These allosteric mutations mimic the ligand activation or inhibition of the TRPML1 channel without interfering with ligand binding and both mutant channels are susceptible to agonist or antagonist modulation, making them better targets for screening potent TRPML1 activators and inhibitors. We also determined the high-resolution structure of TRPML1 in complex with the PI(4,5)P2 inhibitor, revealing the structural basis underlying this lipid inhibition. In addition, an endogenous phospholipid likely from sphingomyelin is identified in the PI(4,5)P2-bound TRPML1 structure at the same hotspot for agonists and antagonists, providing a plausible structural explanation for the inhibitory effect of sphingomyelin on agonist activation.

ω-Grammotoxin-SIA inhibits voltage-gated Na+ channel currents.

ω-Grammotoxin-SIA (GrTX-SIA) was originally isolated from the venom of the Chilean rose tarantula and demonstrated to function as a gating modifier of voltage-gated Ca2+ (CaV) channels. Later experiments revealed that GrTX-SIA could also inhibit voltage-gated K+ (KV) channel currents via a similar mechanism of action that involved binding to a conserved S3-S4 region in the voltage-sensing domains (VSDs). Since voltage-gated Na+ (NaV) channels contain homologous structural motifs, we hypothesized that GrTX-SIA could inhibit members of this ion channel family as well. Here, we show that GrTX-SIA can indeed impede the gating process of multiple NaV channel subtypes with NaV1.6 being the most susceptible target. Moreover, molecular docking of GrTX-SIA onto NaV1.6, supported by a p.E1607K mutation, revealed the voltage sensor in domain IV (VSDIV) as being a primary site of action. The biphasic manner in which current inhibition appeared to occur suggested a second, possibly lower-sensitivity binding locus, which was identified as VSDII by using KV2.1/NaV1.6 chimeric voltage-sensor constructs. Subsequently, the NaV1.6p.E782K/p.E838K (VSDII), NaV1.6p.E1607K (VSDIV), and particularly the combined VSDII/VSDIV mutant lost virtually all susceptibility to GrTX-SIA. Together with existing literature, our data suggest that GrTX-SIA recognizes modules in NaV channel VSDs that are conserved among ion channel families, thereby allowing it to act as a comprehensive ion channel gating modifier peptide.

Feature differences reduction and specific features preserving network for RGB-T salient object detection

In RGB-T salient object detection, effective utilization of the different characteristics of RGB and thermal modalities is essential to achieve accurate detection. Most of the previous methods usually only focus on reducing the differences between modalities, which may ignore the specific features that are crucial for salient object detection, leading to suboptimal results. To address the above issue, an RGB-T SOD network that simultaneously considers the reduction of modality differences and the preservation of specific features is proposed. Specifically, we construct a modality differences reduction and specific features preserving module (MDRSFPM) which aims to bridge the gap between modalities and enhance the specific features of each modality. In MDRSFPM, the dynamic vector generated by the interaction of RGB and thermal features is used to reduce modality differences, and then a dual branch is constructed to deal with the RGB and thermal modalities separately, employing a combination of channel-level and spatial-level operations to preserve their respective specific features. In addition, a multi-scale global feature enhancement module (MGFEM) is proposed to enhance global contextual information to provide guidance information for the subsequent decoding stage, so that the model can more easily localize the salient objects. Furthermore, our approach includes a fully fusion and gate module (FFGM) that utilises dynamically generated importance maps to selectively filter and fuse features during the decoding process. Extensive experiments demonstrate that our proposed model surpasses other state-of-the-art models on three publicly available RGB-T datasets remarkably. Our code will be released at https://github.com/JOOOOKII/FRPNet.

Low-Symmetry Van der Waals Dielectric GaInS3 Triggered 2D MoS2 Giant Anisotropy via Symmetry Engineering.

Low-symmetry structures in van der Waals materials have facilitated the advancement of anisotropic electronic and optoelectronic devices. However, the intrinsic low symmetry structure exhibits a small adjustable anisotropy ratio (1-10), which hinders its further assembly and processing into high-performance devices. Here, a novel 2D anisotropic dielectric, GaInS3 (GIS), which induces isotropic MoS2 to exhibit significant anisotropic optical and electrical responses is demonstrated. With the excellent gate modulation ability of 2D GIS (dielectric constant k ∼12), MoS2 field effect transistor (FET) shows an adjustable conductance ratio from isotropic to anisotropic under dual-gate modulation, up to 106. Theoretical calculations indicate that anisotropy originates from lattice mismatch-induced charge density deformation at the interface. Moreover, the MoS2/GIS photodetector demonstrates high responsivity (≈4750 A W-1) and a large dichroic ratio (≈167). The anisotropic van der Waals dielectric GIS paves the way for the development of 2D transition metal dichalcogenides (TMDCs) in the fields of anisotropic photonics, electronics, and optoelectronics.

Referring Image Segmentation with Multi-Modal Feature Interaction and Alignment Based on Convolutional Nonlinear Spiking Neural Membrane Systems.

Referring image segmentation aims to accurately align image pixels and text features for object segmentation based on natural language descriptions. This paper proposes NSNPRIS (convolutional nonlinear spiking neural P systems for referring image segmentation), a novel model based on convolutional nonlinear spiking neural P systems. NSNPRIS features NSNPFusion and Language Gate modules to enhance feature interaction during encoding, along with an NSNPDecoder for feature alignment and decoding. Experimental results on RefCOCO, RefCOCO[Formula: see text], and G-Ref datasets demonstrate that NSNPRIS performs better than mainstream methods. Our contributions include advances in the alignment of pixel and textual features and the improvement of segmentation accuracy.

TRPML1 gating modulation by allosteric mutations and lipids.

Transient Receptor Potential Mucolipin 1 (TRPML1) is a lysosomal cation channel whose loss-of-function mutations directly cause the lysosomal storage disorder mucolipidosis type IV (MLIV). TRPML1 can be allosterically regulated by various ligands including natural lipids and small synthetic molecules and the channel undergoes a global movement propagated from ligand-induced local conformational changes upon activation. In this study, we identified a functionally critical residue, Tyr404, at the C-terminus of the S4 helix, whose mutations to tryptophan and alanine yield gain- and loss-of-function channels, respectively. These allosteric mutations mimic the ligand activation or inhibition of the TRPML1 channel without interfering with ligand binding and both mutant channels are susceptible to agonist or antagonist modulation, making them better targets for screening potent TRPML1 activators and inhibitors. We also determined the high-resolution structure of TRPML1 in complex with the PI(4,5)P2 inhibitor, revealing the structural basis underlying this lipid inhibition. In addition, an endogenous phospholipid likely from sphingomyelin is identified in the PI(4,5)P2-bound TRPML1 structure at the same hotspot for agonists and antagonists, providing a plausible structural explanation for the inhibitory effect of sphingomyelin on agonist activation.

Gate modulation of barrier height of unipolar vertically stacked monolayer ReS2/MoS2 heterojunction

This study investigates vertically stacked CVD grown ReS2/MoS2 unipolar heterostructure device as Field Effect Transistor (FET) device wherein ReS2 on top acts as drain and MoS2 at bottom acts as source. The electrical measurements of ReS2/MoS2 FET device were carried out and variation in Ids (drain current) Vs Vds (drain voltage) for different Vgs (gate voltage) revealing the n-type device characteristics. Furthermore, the threshold voltage was calculated at the gate bias voltage corresponding to maximum transconductance (gm) value which is ~ 12 V. The mobility of the proposed ReS2/MoS2 heterojunction FET device was calculated as 60.97 cm2 V−1 s−1. The band structure of the fabricated vDW heterostructure was extracted utilizing ultraviolet photoelectron spectroscopy and the UV–visible spectroscopy revealing the formation of 2D electron gas (2DEG) at the ReS2/MoS2 interface which explains the high carrier mobility of the fabricated FET. The field effect behavior is studied by the modulation of the barrier height across heterojunction and detailed explanation is presented in terms of the charge transport across the heterojunction.

Regulation of Kv1.2 redox-sensitive gating by the transmembrane lectin LMAN2.

Kv1.2 potassium channels influence excitability and action potential propagation in the nervous system. Unlike closely-related Kv1 channels, Kv1.2 exhibits highly variable voltage-dependence of gating, attributed to regulation by unidentified extrinsic factors. Variability of Kv1.2 gating is strongly influenced by the extracellular redox potential, and we demonstrate that Kv1.2 currents in dorsal root ganglion sensory neurons exhibit similar variability and redox sensitivity as observed when the channel is heterologously expressed in cell lines. We used a functional screening approach to test the effects of candidate regulatory proteins on Kv1.2 gating, using patch clamp electrophysiology. Among 52 candidate genes tested, we observed that co-expression with the transmembrane lectin LMAN2 led to a pronounced gating shift of Kv1.2 activation to depolarized voltages in CHO and L(tk-) cell lines, accompanied by deceleration of activation kinetics. Overexpression of LMAN2 promoted a slow gating mode of Kv1.2 that mimics the functional outcomes of extracellular reducing conditions, and enhanced sensitivity to extracellular reducing agents. In contrast, shRNA-mediated knockdown of endogenous LMAN2 in cell lines reduced Kv1.2 redox sensitivity and gating variability. Kv1.2 sensitivity to LMAN2 is abolished by mutation of neighboring residues F251 and T252 in the intracellular S2-S3 linker, and these also abolish redox-dependent gating changes, suggesting that LMAN2 influences the same pathway as redox for Kv1.2 modulation. In conclusion, we identified LMAN2 as a candidate regulatory protein that influences redox-dependent modulation of Kv1.2, and clarified the structural elements of the channel that are required for sensitivity.

On the implementation of acollinearity in PET Monte Carlo simulations

Objective.Acollinearity of annihilation photons (APA) introduces spatial blur in positron emission tomography (PET) imaging. This phenomenon increases proportionally with the scanner diameter and it has been shown to follow a Gaussian distribution. This last statement can be interpreted in two ways: the magnitude of the acollinearity angle, or the angular deviation of annihilation photons from perfect collinearity. As the former constitutes the partial integral of the latter, a misinterpretation could have significant consequences on the resulting spatial blurring. Previous research investigating the impact of APA in PET imaging has assumed the Gaussian nature of its angular deviation, which is consistent with experimental results. However, a comprehensive analysis of several simulation software packages for PET data acquisition revealed that the magnitude of APA was implemented as a Gaussian distribution.Approach.We quantified the impact of this misinterpretation of APA by comparing simulations obtained with GATE, which is one of these simulation programs, to an in-house modification of GATE that models APA deviation as following a Gaussian distribution.Main results.We show that the APA misinterpretation not only alters the spatial blurring profile in image space, but also considerably underestimates the impact of APA on spatial resolution. For an ideal PET scanner with a diameter of 81 cm, the APA point source response simulated under the first interpretation has a cusp shape with 0.4 mm FWHM. This is significantly different from the expected Gaussian point source response of 2.1 mm FWHM reproduced under the second interpretation.Significance.Although this misinterpretation has been found in several PET simulation tools, it has had a limited impact on the simulated spatial resolution of current PET scanners due to its small magnitude relative to the other factors. However, the inaccuracy it introduces in estimating the overall spatial resolution of PET scanners will increase as the performance of newer devices improves.

A Novel End-to-End Deep Learning Framework for Chip Packaging Defect Detection.

As semiconductor chip manufacturing technology advances, chip structures are becoming more complex, leading to an increased likelihood of void defects in the solder layer during packaging. However, identifying void defects in packaged chips remains a significant challenge due to the complex chip background, varying defect sizes and shapes, and blurred boundaries between voids and their surroundings. To address these challenges, we present a deep-learning-based framework for void defect segmentation in chip packaging. The framework consists of two main components: a solder region extraction method and a void defect segmentation network. The solder region extraction method includes a lightweight segmentation network and a rotation correction algorithm that eliminates background noise and accurately captures the solder region of the chip. The void defect segmentation network is designed for efficient and accurate defect segmentation. To cope with the variability of void defect shapes and sizes, we propose a Mamba model-based encoder that uses a visual state space module for multi-scale information extraction. In addition, we propose an interactive dual-stream decoder that uses a feature correlation cross gate module to fuse the streams' features to improve their correlation and produce more accurate void defect segmentation maps. The effectiveness of the framework is evaluated through quantitative and qualitative experiments on our custom X-ray chip dataset. Furthermore, the proposed void defect segmentation framework for chip packaging has been applied to a real factory inspection line, achieving an accuracy of 93.3% in chip qualification.

Gate Modulation of Dissipationless Nonlinear Quantum Geometric Current in 2D Te.

Two-dimensional trigonal tellurium (2D Te), a narrow-bandgap semiconductor with a bandgap of approximately 0.3 eV, hosts Weyl points near the band edge and exhibits a narrow, strong Berry curvature dipole (BCD). By applying a back-gate bias to align the Fermi level with the BCD, a sharp increase in the dissipationless transverse nonlinear Hall response is observed in 2D Te. Gate modulation of the BCD demonstrates an on/off ratio of 104 and a responsivity of nearly 106 V/W, while the longitudinal current induced by band modulation reaches an on/off ratio of about 10. This current is sustained up to 200 K, exhibiting a change of 3 orders of magnitude. The inclusion of both transistor action and rectification enhances the temperature sensitivity of the dissipationless Hall current, offering potential applications in electrothermal detectors and sensors and highlighting the significance of topological properties in advancing electronic applications.