Search Results (43)

Parkinson’s disease (PD) is a neurodegenerative disorder with an increasing incidence rate that significantly affects patients’ motor functions and quality of life. Involuntary upper limb tremors (ULTs) commonly manifest unilaterally, affecting either the left or right upper limb. Clinically, ULT frequencies can be categorized into three distinct classes: low-frequency (<4.0 Hz), mid-frequency (4.0–7.0 Hz), and high-frequency (>7.0 Hz) tremors. These tremor motions are characterized by oscillatory or rotational (angular displacement) movements, commonly referred to as the micro-Doppler effect (mDE). This study aims to develop a short-range (<1.0 m) and contactless sensing method for ULT detection based on Doppler millimeter-wave (mm-Wave) radar. The reflected electromagnetic waves indicate time-varying frequency characteristics, which can be analyzed by using time–frequency transform (TFT) methods, such as the Wigner–Ville distribution (WVD) and smoothed pseudo WVD (SPWVD). These TFT methods are employed to extract mDE features, which are subsequently visualized as color-coded spectrograms for ULT classification. Then, a two-dimensional (2D) convolutional neural network (CNN) is employed to automatically recognize the visual feature patterns for ULTs classification based on frequency and amplitude information. In the experimental setup, the W-band (76–81 GHz) Doppler mm-Wave biosensor is implemented for sensing and extracting feature patterns. The proposed classifiers based on “WVD + 2D CNN” and “SPWVD + 2D CNN” are trained and validated by using the collected datasets, with 60% randomly selected for training datasets and 40% for testing datasets in each fold validation. A 10-fold cross-validation method is applied to evaluate the classifier’s performances, achieving an average precision of 95.92 ± 0.60%, average recall of 95.89 ± 0.62%, average F1-score of 0.9588 ± 0.0060, and average accuracy of 95.89 ± 0.62%, respectively. The experimental results demonstrate the feasibility of the proposed classifier for real-time ULTs classification in PD patients using short-range (<1.0 m) and contactless sensing. Full article

Brain-on-a-chip (BOC) refers to a miniaturized in vitro platform that integrates living neuronal networks on a micro-engineered chip, enabling the simulation of brain functions, neural activities and physiological responses. BOC technology is an advanced evolution of microphysiological systems (MPS) and Lab-on-a-Chip platforms, providing novel paradigms for in vitro modeling and exploring early-stage biocomputing by interfacing living neural networks with engineered electronics. Microelectrode arrays (MEAs) serve as the critical physical interface for bidirectional communication in these systems. In this review, we systematically examine the technological landscape and engineering requirements of MEAs tailored for BOC applications, evaluating them across electrical characteristics, structural properties, and biocompatibility. Two primary classes of current MEA technologies, including planar arrays for 2D neural cultures and 3D flexible arrays for brain organoids, are discussed in detail. We highlight the transition from passive planar electrodes to high-density active CMOS and TFT-based arrays, and detail how 3D flexible MEAs utilize endogenous integration and exogenous wrapping strategies to overcome tissue-mechanics mismatches. Furthermore, the integration of MEAs with microfluidics, optoelectronics, and electrochemical sensors to enable multimodal monitoring is explored. With the advantages of the various MEAs, the application of MEAs for BOC, particularly in biological computing and network plasticity research, is discussed. Finally, future technological developments in scalability bottlenecks, chronic stability, and the incorporation of artificial intelligence for MEAs of BOC are prospected. Full article

Background/Objectives: Evidence regarding the relationship between thyroid function tests (TFTs) and severe or treatment-resistant depression in euthyroid individuals remains limited. We aimed to investigate thyroid function tests (TFTs) in euthyroid patients with depression undergoing electroconvulsive therapy (ECT), evaluate associations with ECT response and depression severity, and explore whether clinically meaningful subgroups with differential thyroid function patterns can be identified. Methods: In this retrospective cohort study, we screened 107 inpatients who received ECT for severe or treatment-resistant depression (major depressive disorder [MDD] or bipolar disorder [BD]). Seventy-six euthyroid patients were analyzed. Clinical data, Hamilton Depression Rating Scale (HAMD) scores, and TFTs (TSH, free-T3, and free-T4) were assessed. Logistic regression, multiple linear regression and unsupervised hierarchical cluster analyses were performed. The cluster analysis used clinical and demographic variables, excluding TFTs to avoid circularity and allow thyroid parameters to be examined as secondary biological correlates. Results: The TFT results were not significantly associated with ECT response in euthyroid patients. The multiple linear regression revealed that the baseline HAMD scores were positively associated with free-T4 (β = 0.797, p = 0.001). Hierarchical clustering identified two subgroups; one group characterized by male sex, psychotic features, and MDD diagnosis exhibited lower TSH levels (2.12 vs. 1.49 mlU/L, Cohen’s d = 0.56) despite similar ECT response rates. Conclusions: Subtle TFT variations were not associated with ECT response but were related to depression severity and clinical phenotypes. These findings suggest that normal-range thyroid hormone variability may reflect state-related neuroendocrine patterns rather than predictors of treatment outcome. Our results should be regarded as hypothesis-generating and underline the need for prospective studies to clarify the clinical significance of thyroid function variability in severe depression. Full article

Channel length modulation (CLM) in indium gallium zinc oxide (IGZO) thin film transistors (TFTs) reduces the output resistance (r_o) in the saturation regime. It also degrades current driving accuracy for active matrix organic light emitting diode (AMOLED) backplanes. For top gate, self-aligned devices with nominal channel lengths of 5–15 μm, transmission line method (TLM) analysis yields an effective channel length reduction (ΔL) of about 1.8 μm. This result is consistent with lateral hydrogen redistribution from the self-aligned source/drain (S/D) process. At L = 5 μm, the conventional TFT exhibits r_o = 13.5 ± 2.5 MΩ and an Early voltage (V_A) = 56.1 ± 10.4 V (n = 5). We propose a source connected bottom gate (SCBG) structure that electrostatically stabilizes the pinch-off region and suppresses CLM. The SCBG TFT increases r_o to 475 ± 52 MΩ and V_A to 1159 ± 173 V at L = 5 μm (n = 5), while maintaining normal transfer characteristics. Two-dimensional device simulations reproduce the trend and show that the drain-bias-induced pinch-off shift is reduced, with d(ΔL)/dV_DS decreasing from 0.027 to 0.012 μm/V (about 55%). These results indicate that the SCBG approach is effective for enhancing current saturation in short channel IGZO TFTs for high-resolution AMOLED applications. Full article

In the last few years, Thin Film Transistors (TFTs) based on materials such as amorphous Indium–Gallium–Zinc Oxide (a-IGZO) have gained interest in large-area and low-cost electronics due to their high carrier mobility, high on/off current ratio, low off-state current, and steep subthreshold slope. These characteristics make IGZO TFTs suitable for radio-frequency identification (RFID) tags, analog-to-digital converters (ADCs), logic circuits, sensors, and analog components, including operational amplifiers (OPAMPs). This work presents the implementation and characterization of an OPAMP based on n-type a-IGZO TFTs fabricated on glass substrate. Two previously reported design strategies were integrated: a positive feedback network to increase the output impedance and a topology to enhance the transconductance of the driver transistors, both in the differential input stage. A gain of 26 dB, a bandwidth of 2.4 kHz, a gain–bandwidth product (GBWP) of 48 kHz, and a phase margin of 64° were obtained, which confirms the reliability of the design and the fabrication process. Full article

With the discovery of amorphous oxide semiconductors, a new era of electronics opened. Indium gallium zinc oxide (IGZO) overcame the problems of amorphous and poly-silicon by reaching mobilities of ~10 cm²/Vs and demonstrating thin-film transistors (TFTs) are easy to manufacture on transparent and flexible substrates. However, mobilities over 30 cm²/Vs have been difficult to reach and other materials have been introduced. Recently, polycrystalline In₂O₃ has demonstrated breakthroughs in the field. In₂O₃ TFTs have attracted attention because of their high mobility of over 100 cm²/Vs, which has been achieved multiple times, and because of their use in scaled devices with channel lengths down to 10 nm for high integration in back-end-of-the-line (BEOL) applications and others. The present review focuses first on the material properties with the understanding of the bandgap value, the importance of the position of the charge neutrality level (CNL), the doping effect of various atoms (Zr, Ge, Mo, Ti, Sn, or H) on the carrier concentration, the optical properties, the effective mass, and the mobility. We introduce the effects of the non-parabolicity of the conduction band and how to assess them. We also introduce ways to evaluate the CNL position (usually at ~E_C + 0.4 eV). Then, we describe TFTs’ general properties and parameters, like the field effect mobility, the subthreshold swing, the measurements necessary to assess the TFT stability through positive and negative bias temperature stress, and the negative bias illumination stress (NBIS), to finally introduce In₂O₃ TFTs. Then, we will introduce vacuum and non-vacuum processes like spin-coating and liquid metal printing. We will introduce the various dopants and their applications, from mobility and crystal size improvements with H to NBIS improvements with lanthanides. We will also discuss the importance of device engineering, introducing how to choose the passivation layer, the source and drain, the gate insulator, the substrate, but also the possibility of advanced engineering by introducing the use of dual gate and 2 DEG devices on the mobility improvement. Finally, we will introduce the recent breakthroughs where In₂O₃ TFTs are integrated in neuromorphic applications and 3D integration. Full article

This work presents a low-cost and scalable method for fabricating thin-film transistors (TFTs) using a single-step, 3D-printed shadow mask approach. Room temperature growth of both aluminum-doped zinc oxide (AZO) thin film was used as the semiconductor channel, and zirconium oxide (ZrO₂) as the high-k dielectric, and the films were never exposed to any post-annealing treatment. Structural and morphological characterization confirmed smooth, compact films with stable dielectric behavior. Electrical measurements revealed a field-effect mobility of 13.1 cm²/V·s, a threshold voltage of ~4.1 V, and an on/off ratio of ~10⁴, validating effective gate modulation and drain current saturation. The off-state current, estimated from AZO conductivity measurements, was ~10⁻¹⁰ A, while the on-state current reached ~10⁻⁶ A. Benchmarking against state-of-the-art devices shows that these transistors rival ALD-processed IGZO TFTs and significantly outperform reported indium-free ZnO/AZO devices, while avoiding scarce indium and costly high-temperature or photolithographic processing. These findings establish 3D-printed shadow masks as a practical alternative to conventional lithography for oxide TFT fabrication. The method offers high device performance with simplified, indium-free, and room-temperature processing, underscoring its potential for scalable, transparent, and flexible electronics. Full article

The optical, electrical, and material properties of In–Zn–O (IZO) films were optimized by adjusting the deposition power and annealing temperature. Films deposited at 125 W and annealed at 300 °C exhibited the best performance, with the lowest resistivity (1.43 × 10⁻³ Ω·cm), highest mobility (11.12 cm²/V·s), and highest carrier concentration (4.61 × 10²⁰ cm⁻³). The average transmittance and optical energy gap were 82.57% and 3.372 eV, respectively. The electrical characteristics of amorphous In-Ga-Zn-O (a-IGZO) thin-film transistors (TFTs) using IZO source-drain (S–D) electrodes with various sputtering powers and annealing temperatures were investigated. The optimal sputtering power of 125 W and annealing temperature of 300 °C for the IZO S–D electrodes resulted in the highest field-effect mobility (~12.31 cm²/V·s) and on current (~2.09 × 10⁻⁶ A). This improvement is attributed to enhanced carrier concentration and mobility, which result from the high In/Zn ratio, the larger grain size, and low RMS roughness in the IZO films. The parasitic contact resistance (R_SD) and channel resistance (R_CH) were analyzed using the total resistance method. R_SD decreased with increasing IZO S–D sputtering power, while R_CH reached a minimum at 125 W. Both resistances decreased significantly as the annealing temperature increased from 200 °C to 300 °C. Full article

The increasing density and complexity of electromagnetic signals have brought new challenges to multi-component radar signal recognition. To address the problem of low recognition accuracy under low signal-to-noise ratios (SNR) in adapting the common recognition framework of combining time–frequency transformations (TFTs) with convolutional neural networks (CNNs), this paper proposes a new dual-component radar signal recognition framework (TFGM-RMNet) that combines a deep time–frequency generation module with a Transformer-based residual network. First, the received noisy signal is preprocessed. Then, the deep time–frequency generation module is used to learn the complete basis function to obtain various TF features of the time signal, and the corresponding time–frequency representation (TFR) is output under the supervision of high-quality images. Next, a ResNet combined with cascaded multi-head attention (MHSA) is applied to extract local and global features from the TFR. Finally, modulation format prediction is achieved through multi-label classification. The proposed framework does not require explicit TFT during testing, and the TFT process is built into TFGM to replace the traditional TFT. The classification results and ideal TFR are obtained during testing, realizing an end-to-end deep learning (DL) framework. The simulation results show that, when SNR > −8 dB, this method can achieve an average recognition accuracy close to 100%. It achieves 97% accuracy even at an SNR of −10 dB. At the same time, under low SNR, the recognition performance is better than the existing algorithms including DCNN-RAMIML, DCNN-MLL, and DCNN-MIML. Full article

A novel approach to fabricating a transparent and flexible one-transistor–one-diode (1T-1D) image sensor array on a flexible colorless polyimide (CPI) film substrate is successfully demonstrated with laser lift-off (LLO) techniques. Leveraging transparent indium tin oxide (ITO) electrodes and amorphous indium gallium zinc oxide (a-IGZO) channel-based thin-film transistor (TFT) backplanes, vertically stacked p-i-n hydrogenated amorphous silicon (a-Si:H) photodiodes (PDs) utilizing a low-temperature (<90 °C) deposition process are integrated with a densely packed 14 × 14 pixel array. The low-temperature-processed a-Si:H photodiodes show reasonable performance with responsivity and detectivity for 31.43 mA/W and 3.0 × 10¹⁰ Jones (biased at −1 V) at a wavelength of 470 nm, respectively. The good mechanical durability and robustness of the flexible image sensor arrays enable them to be attached to a curved surface with bending radii of 20, 15, 10, and 5 mm and 1000 bending cycles, respectively. These studies show the significant promise of utilizing highly flexible and rollable active-matrix technology for the purpose of dynamically sensing optical signals in spatial applications. Full article

Channel identification is a useful function to support wireless telecommunication operations because the knowledge of the radio frequency propagation channel characteristics can improve communication efficiency and robustness. In recent times, the application of machine learning (ML) algorithms to the problem of channel identification has been proposed in the literature. In particular, Deep Learning (DL) has demonstrated superior performance to ’shallow’ machine learning algorithms for many wireless communication functions. Inspired by the success of DL in literature, the authors in this paper apply Convolutional Neural Networks (CNN) to the problem of channel identification, which is still an emerging research area. CNN is a deep learning algorithm that has demonstrated superior performance to ML algorithms, in particular for image processing tasks. Because the digitized RF signal is a one-dimensional time series, different algorithms are applied to convert the time series to images using various Time Frequency Transform (TFT) including the CWTs, spectrogram, and Wigner Ville distribution. The images are then provided as input to the CNN. The approach is applied to a data set based on weather radar pulse signals generated in the laboratory of the author’s facilities on which different fading models are applied. These models are inspired by the tap-delay-line 3GPP configurations defined in the standards, but they have been customized with Nakagami-m fading distribution (3GPP-like fading models). The results show the superior performance of time–frequency CNN in comparison to 1D CNN for different values of Signal to Noise Ratio (SNR) in dB. In particular, the study shows that the Continuous Wavelet Transform (CWT) has the optimal performance in this data set, but the choice of the mother wavelet remains a problem to be solved (this is a well-known problem in the research literature). Then, this study also proposes an adaptive technique for the choice of the optimal mother wavelet, which is evaluated on the mentioned data set. The results show that the adaptive proposed approach is able to obtain the optimal performance for most of the SNR conditions. Full article

Amorphous InGaZnO thin film transistors (a-IGZO TFTs) with double-stacked channel layers (DSCL) were quite fit for ultraviolet (UV) light detection, where the best DSCL was prepared by the depositions of oxygen-rich (OR) IGZO followed by the oxygen-deficient (OD) IGZO films. We investigated the influences of oxygen partial pressure (P_O) for DSCL-TFTs on their sensing abilities by experiments as well as Technology Computer Aided Design (TCAD) simulations. With the increase in P_O values for the DSCL depositions, the sensing parameters, including photogenerated current (I_photo), sensitivity (S), responsivity (R), and detectivity (D*) of the corresponding TFTs, apparently degraded. Compared with P_O variations for the OR-IGZO films, those for the OD-IGZO depositions more strongly influenced the sensing performances of the DSCL-TFT UV light detectors. The TCAD simulations showed that the variations of the electron concentrations (or oxygen vacancy (V_O) density) with P_O values under UV light illuminations might account for these experimental results. Finally, some design guidelines for DSCL-TFT UV light detectors were proposed, which might benefit the potential applications of these novel semiconductor devices. Full article

In this study, a bottom-gated ZnO thin film transistor (TFT) pressure sensor with nanorods (NRs) is suggested. The NRs are formed on a planar channel of the TFT by hydrothermal synthesis for the mediators of pressure amplification. The fabricated devices show enhanced sensitivity by 16~20 times better than that of the thin film structure because NRs have a small pressure transmission area and causes more strain in the underlayered piezoelectric channel material. When making a sensor with a three-terminal structure, the leakage current in stand-by mode and optimal conductance state for pressure sensor is expected to be controlled by the gate voltage. A scanning electron microscope (SEM) was used to identify the nanorods grown by hydrothermal synthesis. X-ray diffraction (XRD) was used to compare ZnO crystallinity according to device structure and process conditions. To investigate the effect of NRs, channel mobility is also extracted experimentally and the lateral flow of current density is analyzed with simulation (COMSOL) showing that when the piezopotential due to polarization is formed vertically in the channel, the effective mobility is degraded. Full article

The recent increase in renewable energy adoption has enhanced the penetration rate of electronic equipment, leading to an increased risk of wideband oscillations. Existing wide-area measurement systems mainly focus on fundamental phasors, which cannot effectively monitor wideband oscillations. This study presents an accurate wideband oscillation monitoring method based on radial basis function (RBF) neural networks and Taylor–Fourier transform (TFT). First, discrete Fourier transform is used to obtain a preliminary estimation of the oscillation signal, and then, TFT is adopted to obtain a precise estimation even under dynamic conditions. To reduce the computational burden of TFT, an RBF neural network is used for noise intensity estimation, which adaptively determines the window length. Finally, the proposed method is verified by synthetic data and the field data collected from Guyuan and Hami, China. The experimental results show that the RBF neural network has an excellent denoising effect. When the signal-to-noise ratio is 45 dB, the maximum overall phasor error and the maximum frequency error are 1% and 0.01 Hz, respectively. Hence, it is expected to be useful for next-generation monitoring systems. Full article

We propose an ambipolar chitosan synaptic transistor that effectively responds to binary neuroplasticity. We fabricated the synaptic transistors by applying a chitosan electric double layer (EDL) to the gate insulator of the excimer laser annealed polycrystalline silicon (poly-Si) thin-film transistor (TFT) with Ni-silicide (NiSi) Schottky-barrier source/drain (S/D) junction. The undoped poly-Si channel and the NiSi S/D contact allowed conduction by electrons and holes, resulting in artificial synaptic behavior in both p-type and n-type regions. A slow polarization reaction by the mobile ions such as anions (CH₃COO⁻ and OH⁻) and cations (H⁺) in the chitosan EDL induced hysteresis window in the transfer characteristics of the ambipolar TFTs. We demonstrated the excitatory post-synaptic current modulations and stable conductance modulation through repetitive potentiation and depression pulse. We expect the proposed ambipolar chitosan synaptic transistor that responds effectively to both positive and negative stimulation signals to provide more complex information process versatility for bio-inspired neuromorphic computing systems. Full article