It has been shown that a ferroelectric material integrated into the gate stack of a transistor can create an effective negative capacitance (NC) that allows the device to overcome "Boltzmann tyranny". While this switching below the thermal limit has been observed with Si-based NC field-effect transistors (NC-FETs), the adaptation to 2D materials would enable a device that is scalable in operating voltage as well as size. In this work, we demonstrate sustained sub-60 mV/dec switching, with a minimum subthreshold swing (SS) of 6.07 mV/dec (average of 8.03 mV/dec over 4 orders of magnitude in drain current), by incorporating hafnium zirconium oxide (HfZrO or HZO) ferroelectric into the gate stack of a MoS 2D-FET. By first fabricating and characterizing metal-ferroelectric-metal capacitors, the MoS is able to be transferred directly on top and characterized with both a standard and a negative capacitance gate stack. The 2D NC-FET exhibited marked enhancement in low-voltage switching behavior compared to the 2D-FET on the same MoS channel, reducing the SS by 2 orders of magnitude. A maximum internal voltage gain of ∼28× was realized with ∼12 nm thick HZO. Several unique dependencies were observed, including threshold voltage (V) shifts in the 2D NC-FET (compared to the 2D-FET) that correlate with source/drain overlap capacitance and changes in HZO (ferroelectric) and HfO (dielectric) thicknesses. Remarkable sub-60 mV/dec switching was obtained from 2D NC-FETs of various sizes and gate stack thicknesses, demonstrating great potential for enabling size- and voltage-scalable transistors.

Atomically thin two-dimensional (2D) materials are promising candidates for sub-10 nm transistor channels due to their ultrathin body thickness, which results in strong electrostatic gate control. Properly scaling a transistor technology requires reducing both the channel length (distance from source to drain) and the contact length (distance that source and drain interface with semiconducting channel). Contact length scaling remains an unresolved epidemic for transistor scaling, affecting devices from all semiconductorssilicon to 2D materials. Here, we show that clean edge contacts to 2D MoS 2 provide immunity to the contact-scaling problem, with performance that is independent of contact

Blood pressure monitoring is one avenue to monitor people’s health conditions. Early detection of abnormal blood pressure can help patients to get early treatment and reduce mortality associated with cardiovascular diseases. Therefore, it is very valuable to have a mechanism to perform real-time monitoring for blood pressure changes in patients. In this paper, we propose deep learning regression models using an electrocardiogram (ECG) and photoplethysmogram (PPG) for the real-time estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. We use a bidirectional layer of long short-term memory (LSTM) as the first layer and add a residual connection inside each of the following layers of the LSTMs. We also perform experiments to compare the performance between the traditional machine learning methods, another existing deep learning model, and the proposed deep learning models using the dataset of Physionet’s multiparameter intelligent monitoring in intensive care II (MIMIC II) as the source of ECG and PPG signals as well as the arterial blood pressure (ABP) signal. The results show that the proposed model outperforms the existing methods and is able to achieve accurate estimation which is promising in order to be applied in clinical practice effectively.

Semiconducting carbon nanotube (CNT) networks exhibit electrical, mechanical, and chemical properties attractive for thin-film applications, and printing allows for scalable and economically favorable fabrication of CNT thin-film transistors (TFTs). However, device-to-device variation of printed CNT-TFTs remains a concern, which largely stems from variations in printed CNT thin-film morphology and resulting properties. In this work, we overcome the challenges associated with printing uniformity and demonstrate an aerosol jet printing process that yields devices exhibiting a hole mobility of μ h = 12.5 cm 2 /V•s with a relative standard deviation as small as 4% (from over 38 devices). The enabling factors of such high uniformity include control of the CNT ink bath temperature during printing, ink formulation with nonvolatile and viscosifying additives, and a thermal treatment for polymer removal. It is discovered that a low CNT ink temperature benefits aerosol jet printing uniformity and stability in both shortterm (∼1 min) and long-term (∼1 h) printing settings. These findings shed light on the effect of a commonly overlooked dimension of CNT aerosol jet printing and provide a practical strategy for large-scale, high-consistency realization of CNT-TFTs.

Hafnium zirconium oxide (Hf0.5Zr0.5O2 or HZO) thin films show great promise for enabling ferroelectric field-effect transistors (FeFETs) for memory applications and negative capacitance FETs for low-power digital devices. One challenge in the integration of ferroelectric HZO is the need for specific capping layers to yield the most pronounced ferroelectric behavior; to date, superior HZO devices use titanium nitride or tantalum nitride, which limits HZO integration into various device structures. In this work, the authors demonstrate the use of elemental capping layers, including Pt, Ni, and Pd, for driving ferroelectricity in HZO. Different combinations of these capping metals, along with changes in the HZO thickness and annealing conditions, have yielded the optimal conditions for maximizing ferroelectric behavior. A remnant polarization of 19 μC/cm2 and a coercive field strength of 1.07 MV/cm were achieved with the Pt/HZO/Ni stack annealed at 650 °C with a HZO thickness of ∼20 nm. These results bring even greater promise to the use of HZO in memory and/or digital electronic devices by expanding the toolkit of materials that may be used for realizing ferroelectricity.

1 The formation of advanced glycation endproducts (AGEs) on collagen within the arterial wall may be responsible for the development of diabetic vascular injury. This study was to examine the role of aminoguanidine (AG), an inhibitor of AGEs formation, in the prevention of arterial stiffening and cardiac hypertrophy in streptozotocin (STZ) induced diabetes in rats. 2 Diabetes was induced in animals by a single tail vein injection with 65 mg kg À1 STZ. After confirmation of the development of hyperglycemia (2 days later), rats were treated for 8 weeks with AG (daily peritoneal injections of 50 mg kg À1 ) and compared with the age-matched untreated diabetic controls.3 After exposure to AG, the STZ-diabetic rats showed no alterations in cardiac output, aortic pressure profiles, total peripheral resistance, and aortic characteristic impedance. 4 By contrast, treatment of this experimental diabetes with AG resulted in a significant increase in wave transit time (t), from 20.470.6 to 24.770.5 ms (Po0.05) and a decrease in wave reflection factor (R f ), from 0.7870.04 to 0.5370.02 (Po0.05). The decreased R f associated with the increased t suggest that AG may retard the diabetes-induced augmentation in systolic load of the left ventricle coupled to its arterial system. 5 Meanwhile, the diminished ratio of left ventricular weight to body weight suggests that prevention of the diabetes-related cardiac hypertrophy by AG may correspond to the drug-induced decline in aortic stiffening. 6 Glycation-derived modification on aortic collagen was also found to be enhanced in rats with diabetes ( þ 65.3%, Po0.05) and the advanced glycation process was retarded by AG treatment. 7 We conclude that long-term administration of AG to the STZ-treated rats imparts significant protection against the diabetes-derived deterioration in vascular dynamics, at least partly through inhibition of the AGEs accumulation on collagen in the arterial wall. Abbreviations: AG, aminoguanidine; AGEs, advanced glycation endproducts; BW, body weight (g); C, systemic arterial compliance (ml kg À1 mmHg À1 ); CO, cardiac output (ml kg À1 min À1 ); HR, basal heart rate (beats min À1 ); iNOS, inducible isoform of nitric oxide syntheses; LVW, left ventricular weight (g); NO, nitric oxide; P b , magnitude of the forward pressure (mmHg); P d , diastolic aortic pressure (mmHg); P f , magnitude of the forward pressure (mmHg); P m , mean aortic pressure (mmHg); P s , systolic aortic pressure (mmHg); R f , wave reflection factor; R p , total peripheral resistance (mmHg min kg ml À1 ); SDS-PAGE, sodium dodecyl sulfate-polyacrylamide gel electrophoresis; STZ, streptozotocin; SV, stroke volume (ml kg À1 beat À1 ); Z c , aortic characteristic impedance (mmHg min kg ml À1 ); Z i , aortic input impedance spectra (mmHg min kg ml À1 ); t, wave transit time (ms)

A novel power-line interference (PLI) detection and suppression algorithm is presented to preprocess the electrocardiogram (ECG) signals. A distinct feature of this proposed algorithm is its ability to detect the presence of PLI in the ECG signal before applying the PLI suppression algorithm. No PLI suppression operation will be performed if PLI is not detected. We propose a PLI detector that employs an optimal linear discriminant analysis (LDA) algorithm to make a decision for the PLI presence. An efficient recursive least-squares (RLS) adaptive notch filter is also developed to serve the purpose of PLI suppression. Experimental results demonstrate superior performance of this proposed algorithm.

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