Algorithm prediction of single particle irradiation effect based on novel TFETs.

In order to predict the single particle irradiation of tunnel field effect transistor (TFET) devices, a deep learning algorithm network model was built to predict the key characterization parameters of the single particle transient. Computer aided design (TCAD) technique is used to study the influence of single particle effect on the novel stacked source trench gate TFET device. The results show that with the increase of drain voltage, incident width of heavy ions (less than 0.04µm), and linear energy transfer, the drain transient current and collected charge also increase. The prediction results of deep learning algorithm show that the relative error percentage of drain current pulse peak (IDMAX) and collected charge (Qc) is less than 10%, and the relative error percentage of most predicted values is less than 1%. Comparison experiments with five traditional machine learning methods (support vector machine, decision tree, K-nearest algorithm, ridge regression, linear regression) show that the deep learning algorithm has the best performance and has the smallest average error percentage. This data-driven deep learning algorithm model not only enables researchers who are not familiar with semiconductor devices to quickly obtain the transient data of a single particle under any conditions; at the same time, it can be applied to digital circuit design as a data-driven device model reflecting the reliability of single particle transient. The application of deep learning in the field of device irradiation prediction has a highly promising prospect in the future.

Analysis of coupling effect between TID and SET in SOI Tri-Gate nanowire field-effect transistors.

This study investigates the response of nanowire field-effect transistors (NWFETs) to total ionizing dose (TID), single-event transient (SET), and their coupling effects in junctionless (JL), inversion (IM), and junctionless accumulation (AC) modes. The degradation of the three modes under irradiation and the effect of device bias configuration on the electrical properties of NWFETs are analyzed, and the different effects of SET on the three modes are compared. On this basis, the influence of TID on SET current generation and the charge collection mechanism are studied, and the changes in peak current, pulse width, and collected charge of transient current under different TIDs are compared. The results show that JL mode has the worst resistance to TID and SET coupling effects, followed by IM and AC modes.

A study on the high power microwave effects of PIN diode limiter based on deep learning algorithm.

PIN diodes, due to their simple structure and variable resistance characteristics under high-frequency high-power excitation, are often used in radar front-end as limiters to filter high power microwaves (HPM) to prevent its power from entering the internal circuit and causing damage. This paper carries out theoretical derivation and research on the HPM effects of PIN diodes, and then uses an optimized neural network algorithm to replace traditional physical modeling to calculate and predict two types of HPM limiting indicators of PIN diode limiters. We proposes a neural network model for each of the following two prediction scenarios: in the scenario of time-junction temperature curves under different HPM irradiation, the weighted mean squared error (MSE) between the predicted values from the test dataset and the simulated values is below 0.004. While in predicting PIN limiter's power limitation threshold, insertion loss, and maximum isolation under different HPM irradiation, the MSE of the test set prediction values and simulation values are all less than 0.03. The method proposed in this research, which applies an optimized neural network algorithm to replace traditional physical modeling algorithms for studying the high-power microwave effects of PIN diode limiters, significantly improves the computational and simulation speed, reduces the calculation cost, and provides a new method for studying the high-power microwave effects of PIN diode limiters.

Prediction of electrical properties of GAAFET based on integrated learning model.

As device feature sizes continue to decrease and fin field effect transistors reach their physical limits, gate all around field effect transistors (GAAFETs) have emerged with larger gate control areas and stackable characteristics for better suppression of second-order effects such as short-channel effects due to their gate encircling characteristics. Traditional methods for studying the electrical characteristics of devices are mostly based on the technology computer-aided design. Still, it is not conducive to developing new devices due to its time-consuming and inefficient drawbacks. Deep learning (DL) and machine learning (ML) have been well-used in recent years in many fields. In this paper, we propose an integrated learning model that integrates the advantages of DL and ML to solve many problems in traditional methods. This integrated learning model predicts the direct current characteristics, capacitance characteristics, and electrical parameters of GAAFET better than those predicted by DL or ML methods alone, with a linear regression factor (R2) greater than 0.99 and very small root mean square error. The proposed integrated learning model achieves fast and accurate prediction of GAAFET electrical characteristics, which provides a new idea for device and circuit simulation and characteristics prediction in microelectronics.

Enhancing intracellular mRNA precise imaging-guided photothermal therapy with a nucleic acid-based polydopamine nanoprobe.

Despite significant advancements in cancer research, real-time monitoring and effective treatment of cancer through non-invasive techniques remain a challenge. Herein, a novel polydopamine (PDA) nucleic acid nanoprobe has been developed for imaging signal amplification of intracellular mRNA and precise photothermal therapy guidance in cancer cells. The PDA nucleic acid nanoprobe (PDA@DNA) is constructed by assembling an aptamer hairpin (H1) labeled with the Cy5 fluorophore and another nucleic acid recognition hairpin (H2) onto PDA nanoparticles (PDA NPs), which have exceptionally high fluorescence quenching ability and excellent photothermal conversion properties. The nanoprobe could facilitate cellular uptake of DNA molecules and their protection from nuclease degradation. Upon recognition and binding to the intracellular mRNA target, a catalytic hairpin assembly (CHA) reaction occurs. The stem of H1 unfolds upon binding, allowing the exposed H1 to hybridize with H2, forming a flat and sturdy DNA double-stranded structure that detaches from the surface of PDA NPs. At the same time, the target mRNA is displaced and engages in a new cyclic reaction, resulting in the recovery and significant amplification of Cy5 fluorescence. Using thymidine kinase1 (TK1) mRNA as a model mRNA, this nanoprobe enables the analysis of TK1 mRNA with a detection limit of 9.34 pM, which is at least two orders of magnitude lower than that of a non-amplifying imaging nucleic acid probe. Moreover, with its outstanding performance for in vitro detection, this nanoprobe excels in precisely imaging tumor cells. Through live-cell TK1 mRNA imaging, it can accurately distinguish between tumor cells and normal cells. Furthermore, when exposed to 808-nm laser irradiation, the nanoprobe fully harnesses exceptional photothermal conversion properties of PDA NPs. This results in a localized temperature increase within tumor cells, which ultimately triggers apoptosis in these tumor cells. The integration of PDA@DNA presents innovative prospects for tumor diagnosis and image-guided tumor therapy, offering the potential for high-precision diagnosis and treatment of tumors.

Design and synthesis of a novel chiral photoacoustic probe and accurate imaging detection of hydrogen peroxide in vivo.

Nanocatalytic medicine, which aims to accurately target and effectively treat tumors through intratumoral in situ catalytic reactions triggered by tumor-specific environments or markers, is an emerging technology. However, the relative lack of catalytic activity of nanoenzymes in the tumor microenvironment (TME) has hampered their use in biomedical applications. Therefore, it is crucial to develop a highly sensitive probe that specifically responds to the TME or disease markers in the TME for precision diagnosis and treatment of diseases. In this work, a chiral photoacoustic (PA) nanoprobe (D/L-Ce@MoO3) based on the H2O2-catalyzed TME activation reaction was constructed in a one-step method using D-cysteine (D-Cys) or L-cysteine (L-Cys), polymolybdate, and cerium nitrate as raw materials. The designed and synthesized D/L-Ce@MoO3 chiral nanoprobe can perform in situ, non-invasive, and precise imaging of pharmacological acute liver injury. In vivo and in vitro experiments have shown that the D/L-Ce@MoO3 probe had chiral properties, the CD signal decreased upon reaction with H2O2, and the absorption and PA signals increased with increasing H2O2 concentration. This is because of the catalytic reaction between Ce ions doped in the nanoenzyme and the high expression of H2O2 caused by drug-induced liver injury to produce ·OH, which has a strong oxidizing property to kill tumor cells and destroy the Mo-S bond in the probe, thus converting the chiral probe into an achiral polyoxometalate (POM) with PA signal.

Rational design of a near-infrared dual-emission fluorescent probe for ratiometric imaging of glutathione in cells.

Sensors for which the output signal is an intensity change for a single-emission peak are easily disturbed by many factors, such as the stability of the instrument, intensity of the excitation light, and biological background. However, for ratiometric fluorescence sensors, the output signal is a change in the intensity ratio of two or more emission peaks. The fluorescence intensity of these emission peaks is similarly affected by external factors; thus, these sensors have the ability to self-correct, which can greatly improve the accuracy and reliability of the detection results. To accurately image glutathione (GSH) in cells, gold nanoclusters (AuNCs) with intrinsic double emission at wavelengths of 606 nm and 794 nm were synthesized from chloroauric acid. With the emission peak at 606 nm as the recognition signal and the emission peak at 794 nm as the reference signal, a near-infrared dual-emission ratio fluorescence sensing platform was constructed to accurately detect changes in the GSH concentration in cells. In vitro and in vivo analyses showed that the ratiometric fluorescent probe specifically detects GSH and enables ultrasensitive imaging, providing a new platform for the accurate detection of active small molecules.

Research on electronic synaptic simulation of HfO2-based memristor by embedding Al2O3.

The potential of neuromorphic computing in synaptic simulation has led to a renewed interest in memristor. However, the demand for multilevel resistive switching with high reliability and low power consumption is still a great resistance in this application. In this work, the electronic synaptic plasticity and simulated bipolar switching behavior of Pt/Al2O3(2 nm)/HfO2(10 nm)/Al2O3(2 nm)/Ti tri-layer memristor is investigated. The effect of Al2O3layer embedded at the top electrode and the bottom electrode on the resistive performance of the memristor was studied. It is found that both of them can effectively improve the reliability of the device (104cycles), the resistive window (>103), the tunable synaptic linearity and reduce of the operating voltage. RRAM with Al2O3embedded at the top electrode have higher uniformity and LTP linearity, while those with Al2O3embedded at the bottom electrode significantly reduce the operating current (∼10µA) and improve LTD linearity. Electron transport mechanisms were compared between single-layer HfO2and tri-layer Al2O3/HfO2/Al2O3samples under DC scanning. The results showed that the thin Al2O3layer at the top electrode led to Fowler Northeim tunneling in the low-resistance state, while the thin Al2O3layer at the bottom electrode led to Schottky emission in the high-resistance state. The Al2O3/HfO2/Al2O3memristors were successfully used to achieve synaptic properties, including enhancement, inhibition, and spike time-dependent plasticity, demonstrating an important role in high-performance neuromorphic computing applications.

A Novel Fluorescent Aptamer Sensor with DNAzyme Signal Amplification for the Detection of CEA in Blood.

Carcinoembryonic antigen (CEA) is a tumor-specific biomarker; however, its low levels in the early stages of cancer make it difficult to detect. To address the need for analysis of ultra-low-level substances, we designed and synthesized a fluorescent aptamer sensor with DNAzyme signal amplification and used it for the detection of CEA in blood. In the presence of the target protein, the aptamer sequence in the recognition probe binds to the target protein and opens the hairpin structure, hybridizes with the primer and triggers a polymerization reaction in the presence of polymerase to generate double-stranded DNA with two restriction endonuclease Nb.BbvCl cleavage sites. At the same time, the target protein is displaced and continues to bind to another recognition probe, triggering a new round of polymerization reaction, forming a cyclic signal amplification triggered by the target. The experimental results show that the blood detection with CEA has a high sensitivity and a wide detection range. The detection range: 10 fg/mL~10 ng/mL, with a detection limit of 5.2 fg/mL. In addition, the sensor can be used for the analysis of complex biological samples such as blood.

Fabrication of Ag-CaCO3 Nanocomposites for SERS Detection of Forchlorfenuron.

In this study, Ag-CaCO3 nanocomposites were synthesized using silver nitrate as the precursor solution based on calcium carbonate nanoparticles (CaCO3 NPs). The synthesis involved the reaction of calcium lignosulphonate and sodium bicarbonate. The properties of Ag-CaCO3 nanocomposites were studied by various technologies, including an ultraviolet-visible spectrophotometer, a transmission electron microscope, and a Raman spectrometer. The results showed that Ag-CaCO3 nanocomposites exhibited a maximum UV absorption peak at 430 nm, the surface-enhanced Raman spectroscopy (SERS) activity of Ag-CaCO3 nanocomposites was evaluated using mercaptobenzoic acid (MBA) as the marker molecule, resulting in an enhancement factor of 6.5 × 104. Additionally, Ag-CaCO3 nanocomposites were utilized for the detection of forchlorfenuron. The results demonstrated a linear relationship in the concentration range of 0.01 mg/mL to 2 mg/mL, described by the equation y = 290.02x + 1598.8. The correlation coefficient was calculated to be 0.9772, and the limit of detection (LOD) was determined to be 0.001 mg/mL. These findings highlight the relatively high SERS activity of Ag-CaCO3 nanocomposites, making them suitable for analyzing pesticide residues and detecting toxic and harmful molecules, thereby contributing to environmental protection.

A Circular Dichroism and Photoacoustic Dual-Mode Probe for Detection In Vitro and Imaging In Vivo of Hydroxyl Radicals.

The drug toxicity is a long-term concern in contemporary medical research. Serious drug toxicity may cause acute liver failure or even patient death. Currently, the pathogenesis of drug-induced liver injury (DILI) is not entirely clear, and there is still no specific treatment for patients with DILI. Accordingly, improving the diagnosis and treatment level of DILI is a major challenge facing the basic and clinical research in relevant fields. To address these, an ·OH-activated circular dichroism (CD) and photoacoustic (PA) dual-mode nanoprobe was here designed and synthesized. The probe was prepared using chiral d/l-cysteine and polymolybdate as raw materials to form a nanocomplex with chiral properties (Ox-POM@d/l-Cys) based on the interaction between metal ions and sulfhydryl groups in Ox-POM@d/l-Cys. In vitro and in vivo experimental results have shown that the as-proposed dual-mode nanoprobe can be used not only for CD spectral detection of Fenton's reagent but also for PA imaging monitoring of ·OH. More importantly, inspired by the NIR PA properties, the Ox-POM@d/l-Cys probe was used for the first time to detect ·OH in DILI mice. This will provide very useful information for the diagnosis and treatment of DILI disease.

Mitochondria-Targeted Fluorescence/Photoacoustic Dual-Modality Imaging Probe Tailored for Visual Precise Diagnosis of Drug-Induced Liver Injury.

The multispectral optoacoustic tomography (MSOT) technique can be used to perform high-resolution molecular imaging under deep tissues, which gives the technology significant prospective for clinical application. Here, we developed a superoxide anion (O2•-)-activated MSOT and fluorescence dual-modality imaging probe (APSA) for early diagnosis of drug-induced liver injury (DILI). APSA can respond quickly to O2•-, resulting in an absorption peak blueshift from 845 to 690 nm, which also leads to the photoacoustic (PA) signal at 690 nm and the fluorescence signal at 748 nm increases linearly with increasing O2•- concentration, which can be utilized to assess the extent of liver damage. The developed MSOT imaging method can eliminate background interference from hematopoietic tissue by collecting the PA signals excited at 680, 690, 740, 760, 800, 845, and 900 nm wavelengths to achieve noninvasive in situ visual diagnosis of DILI. The developed fluorescence imaging method can be used for the imaging of endogenous O2•- in living cells and anatomic diagnosis of liver injury. The developed probe has broad application prospects in the early diagnosis of DILI.

Transmission properties of grating-gate GaN-based HEMTs under different incident angles in the mid-infrared region.

The absorption tunability of grating-gate GaN-based HEMTs in the mid-infrared region has been confirmed in wide frequency regions. However, the application potential of grating-gate GaN-based HEMTs is limited due to a lack of study on transmission properties under different incident angles. Therefore, this paper studied the transmission characteristics of grating-gate GaN-based HEMTs under different incident angles in the mid-infrared region. By using the optical transfer matrix approach to model the dispersion characteristics in the structure, we found that the stronger plasmon polaritons and phonon polaritons occur in conductive channel and GaN layer. The variation of different incident wave vectors with incident angle affects the plasmon polaritons and phonon polaritons excitation intensities, resulting in the angular tunability transmission properties of grating-gate GaN-based HEMTs. After simulating the electric field distribution in COMSOL, the different transmission properties of grating-gate GaN-based HEMTs occur under different incident angles. Simulated results reveal the excellent angle-selectivity in grating-gate GaN-based HEMTs. The research into these characteristics shows that the structure has a lot of promise for designing mid-infrared angle selection filters, sensors, and other subwavelength devices in the future.

Polarization properties in grating-gated AlN/GaN HEMTs at mid-infrared frequencies.

The plasmon resonances of grating-gated AlN/GaN HEMTs can occur in wide frequency regions at mid-infrared frequencies. However, the lack of polarization properties research in grating-gated AlN/GaN HEMTs prevents the application potential. In order to solve the problem, the polarization properties in grating-gated AlN/GaN HEMTs at mid-infrared frequencies were studied in the paper. After using the optical transfer matrix method to calculate the dispersion curves in grating-gated AlN/GaN HEMTs, the plasmon polaritons in conductive channel and phonon polaritons in GaN layer occur under TM incident waves rather than TE incident waves. The phenomenon illustrates the potential of polarization-selectivity has existed in grating-gated AlN/GaN HEMTs. To study the polarization properties of grating-gated AlN/GaN HEMTs in detail, the electric field distribution and transmission properties of the structure were simulated in COMSOL. The results show the excellent polarization-selectivity at mid-infrared frequencies in grating-gated AlN/GaN HEMTs. The studies of these characteristics indicate the vast potential for using grating-gated AlN/GaN HEMTs to design mid-infrared polarizers, mid-infrared polarization state modulators and other devices in the future.

A high performance trench gate tunneling field effect transistor based on quasi-broken gap energy band alignment heterojunction.

In this letter, a tunneling field effect transistor based on quasi-broken gap energy band alignment (QB-TFET) is proposed and investigated by simulation method. To offering high on-state current, InGaAs/GaAsSb heterojunction with quasi-broken gap energy band alignment is applied to QB-TFET to improve the band-to-band tunneling rate. Trench gate structure and InGaAs pocket layer are applied to further increase the tunneling efficiency. To suppress the leakage current caused by the off-state tunneling path from source to drain, an intrinsic InGaAs spacer is inserted between n+ InGaAs drain and p+ GaAsSb source. In order to further improve the control ability of gate voltage on channel, TiO2is used as the gate dielectric of the proposed QB-TFET. Moreover, the effect ofxandyfraction of InxGa1-xAs and GaAsySb1-yon quasi-broken gap tunneling junction are studied in this work. The electrical characteristic change of QB-TFET with differentxandyfraction is analyzed. The proposed QB-TFET is compared with other works and shows an obvious advantage on performance. As a result, a large on-state current (Ion) of 921µAµm-1can be obtained. Moreover, steep average subthreshold swing (SSavg) of 4.9 mV/dec can be achieved whenIon = 1µAµm-1.

The accelerated design of the nanoantenna arrays by deep learning.

Nanoantenna fusion photonics and nanotechnology can manipulate light through the ultra-thin structure composed of sub-wavelength antennas, and meet the important requirements for miniaturized optical components, completely changing the field of optics. However, the device design process is still time-consuming and consumes computing resources. Besides, the professional knowledge requirements of engineers are also high. Relying on the algorithm's inference ability and excellent computing ability, artificial intelligence has great potential in the fields of material design, material screening, and device performance prediction. However, the deep learning (DL) requires a mass of data. Therefore, this article proposes a method for the forward and inverse design of nanoantenna based on DL. Compared with the previous work, the network uses a two-dimensional matrix as input, which has a simple structure and is more suitable for the advantages of deep netural network. Simultaneously, the small datasets can be used to achieve higher accuracy. In the forward prediction, 100% of the data error is less than 0.007; in the inverse prediction, the data with error less than 0.05 accounted for 90%, 99.8% and 100% of the length, height, and width's datasets. It demonstrates that the method can improve the automation of the design process and reduce the consumption of computer resources.

Prediction of electrical properties of FDSOI devices based on deep learning.

Fully depleted Silicon on insulator technology (FDSOI) is proposed to solve the various non-ideal effects when the process size of integrated circuits is reduced to 45 nm. The research of traditional FDSOI devices is mostly based on simulation software, which requires a lot of calculation and takes a long time. In this paper, a deep learning (DL) based electrical characteristic prediction method for FDSOI devices is proposed. DL algorithm is used to train the simulation data and establish the relationship between the physical parameters and electrical characteristics of the device. The network structure used in the experiment has high prediction accuracy. The mean square error of electrical parameters and transfer characteristic curve is only 4.34 × 10-4and 2.44 × 10-3respectively. This method can quickly and accurately predict the electrical characteristics of FDSOI devices without microelectronic expertise. In addition, this method can be extended to study the effects of various physical variables on device performance, which provides a new research method for the field of microelectronics.

Single event effects prediction of MOSFET device using deep learning.

Single event effect (SEE) is an important problem in the reliability research of integrated circuits. The study of SEE of traditional MOSFET devices is mainly based on simulation software, which is characterized by slow simulation speed, large computation and time-consuming. In this paper, a SEE research method based on deep learning is proposed. The method relies on 28 nm MOSFET. The complete drain transient current pulse, transient current peak value and total collected charge can be obtained in a short time by inputting relevant parameters that affect the SEE. The accuracy of the network for predicting transient current peak and total collected charge is 96.95% and 97.53% respectively, and the mean goodness of fit of the network for predicting the drain transient current pulse curve is 0.985. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.89 × 103and 1.50 × 103times respectively. This method has good prediction effect and provides a new possibility for the study of SEE.

An Electrochemical Sensor Based on Electropolymerization of ß-Cyclodextrin on Glassy Carbon Electrode for the Determination of Fenitrothion.

An electrochemical sensor enabled by electropolymerization (EP) of ß-cyclodextrin on glassy carbon electrode (ß-CDP/GCE) is built for the determination of fenitrothion (FNT). The effects of the EP cycles, pH value, and enrichment time on the electrochemical response of FNT were studied. With the optimum conditions, good linear relationships between the current of the reduction peak of the nitroso derivative of FNT and the concentration are obtained in the range of 10-150 and 150-4000 ng/mL, with a detection limit of 6 ng/mL (S/N = 3). ß-CDP/GCE also exhibits a satisfactory applicability in cabbage and tap water, with recovery values between 98.43% and 112%. These outstanding results suggest that ß-CDP/GCE could be a new effective alternative for the determination of FNT in real samples.

Investigation of charge trapping mechanism in MoS2field effect transistor by incorporating Al into host La2O3as gate dielectric.

The charge trapping effect plays a key role in multi-bit memory devices and brain-like neuron devices. Herein, MoS2field effect transistors are fabricated, incorporating Al into host La2O3as the gate dielectric, which exhibit excellent electrical properties with an on-off ratio in the memory window of ∼106and a memory window ratio of ∼40%. Furthermore, the charge trapping and de-trapping processes were systematically studied, and the time constants are obtained from time-domain characteristics. Making use of the charge trapping effect, the threshold voltage of the device can be continuously adjusted. The oxide layer trap density and the interface state trap density are extracted using the charge separation method. These theoretical studies provide a deeper understanding of ways to control the charge trapping process, benefitting the commercialization of two-dimensional electronic devices and the development of new charge trapping devices.