On the Critical Role of Ferroelectric Thickness for Negative Capacitance Device-Circuit Interaction

This paper demonstrates the critical role that Ferroelectric (FE) layer thickness (tFE) plays in Negative Capacitance (NC) transistors connecting device and circuit levels together. The study is done through fully-calibrated TCAD simulations for a 14nm FDSOI technology node, exploring the impact of tFE on the figures of merit of n-type and p-type devices, voltage transfer characteristic (VTC) and noise margin of inverter as well as the speed of buffer circuits. First, we analyze the device electrical parameters (e.g., ION, SS, ION/IOFF and Cgg) by varying tFE up to the maximum level at which hysteresis in the I-V characteristic starts. Then, we analyze the deleterious impact of Negative Differential Resistance (NDR), due to the drain to gate coupling, demonstrating how it imposes an additional constraint limiting the maximum tFE. We show the consequences of NDR effects on the VTC and noise margin of inverter, which are essential components for constructing robust clock trees in any chip. We demonstrate how the considerable increase in the gate’s capacitance due to FE seriously degrades the circuit’s performance imposing further constraints limiting the maximum tFE. Further, we analyze the impact of tFE on the SRAM cell static performance metrics such hold noise margin (HNM), read noise margin (RNM) and write noise margin (WNM) at supply voltages of 0.7V and 0.4V. We demonstrate that the HNM and RNM in a NC-FDSOI FET based SRAM cell are higher then those of the baseline FDSOI FET based SRAM cell noise margin and further increase with tFE. However, the WNM in general follows a non monotonic trend w.r.t tFE, and the trend also depends on the supply voltage. Finally, we optimize the design of the SRAM cell considering overall performance metrics. All in all, our analysis provides guidance for device and circuit designers to select the optimal FE thickness for NCFETs in which hysteresis-free operations, reliability, and performance are optimized

Effect of Seed Priming with Some Plant Leaf Extract on Seedling Growth Characteristics and Root Rot Disease in Tomato

Tomato is one of the important vegetable crops. The problem of seedling establishment is found in tomato due to several soil borne diseases. One of them is root rot caused by Fusarium oxysproum. There are many chemical methods available to control this disease, but use of chemicals deplete the soil micro-environment and causes soil and water pollution and also do not fit within the framework of ‘Organic farming’. Seed priming with certain phytochemicals may be an economic and ecofriendly alternative to such chemicals. In present study we primed tomato seeds with leaf extract of six different plants (White musale, Periwinkle, Neem, Wood apple, Lantana and White cedar). Different leaf extracts of dose of 2% was taken independently for seed priming. We found that priming with White musale, Periwinkle, Neem and wood apple leaf extract had an improvement in different seed and seedling growth parameters in presence of pathogen. Priming with Lantana and white cedar leaf extract showed a reduction in some of the parameters that may be due to allelopathic nature of these plants. Seed priming with leaf extract of Wood apple exhibited maximum survival rate (76.50 %) followed by Neem (68.46 %) and White Musale (52.60 %)

Impact of NCFET on Neural Network Accelerators

This is the first work to investigate the impact that Negative Capacitance Field-Effect Transistor (NCFET) brings on the efficiency and accuracy of future Neural Networks (NN). NCFET is at the forefront of emerging technologies, especially after it has become compatible with the existing fabrication process of CMOS. Neural Network inference accelerators are becoming ubiquitous in modern SoCs and there is an ever-increasing demand for tighter and tighter throughput constraints and lower energy consumption. To explore the benefits that NCFET brings to NN inference regarding frequency, energy, and accuracy, we investigate different configurations of the multiply-add (MADD) circuit, which is the core computational unit in any NN accelerator. We demonstrate that, compared to the baseline 7nm FinFET technology, its negative capacitance counterpart reduces the energy by 55%, without any frequency reduction. In addition, it enables leveraging higher computational precision, which results to a considerable improvement in the inference accuracy. Importantly, the achieved accuracy improvement comes also together with a significant energy reduction and without any loss in frequency

Unveiling the Impact of IR-Drop on Performance Gain in NCFET-Based Processors

Negative capacitance field-effect transistor (NCFET) pushes the subthreshold swing beyond its fundamental limit of 60 mV/decade by incorporating a ferroelectric material within the gate-stack of transistor. Such a material manifests itself as an NC that provides an internal voltage amplification for the transistor resulting in higher ON-current levels. Hence, the performance of processors can be boosted while the operating voltage still remains the same. However, having an NC makes the total gate terminal capacitance larger. Although the impact of that on compensating the gained performance has already been studied in the literature, this paper is the first to explore the impact of NC on exacerbating the IR-drop problem in processors. In fact, voltage fluctuation in the power delivery network (PDN) due to IR-drops is one of the prominent sources of performance loss in processors, which necessitates adding timing guardbands to sustain a reliable operation during runtime. In this paper, we study NC-FinFET standard cells and processor for the 7-nm technology node. We demonstrate that NC, on the one hand, results in larger IR-drops due to the increase in current densities across the chip, which leads to a higher stress on the PDN. However, the internal voltage amplification provided by NC, on the other hand, compensates to some degree the voltage reduction caused by IR-drop. We investigate, from physics all the way to full-chip (GDSII) level, how the overall performance of a processor is affected under the impact that NC has on magnifying and compensating IR-drop

Information Content of Implicit Spot Prices Embedded in Single Stock Future Prices: Evidence from Indian Market

This study examines the information content of pricing error, measured by the difference between the implied price computed using the cost of carry model and the spot price of Single Stock Futures (SSFs), traded on National Stock Exchange (NSE), India. The returns of portfolios, based on ranking of such pricing errors, are investigated. The consistency of results is verified by controlling for established risk factors, that is, market, size, value and momentum premium, and idiosyncratic factors such as firm’s liquidity and size. Our study reveals that the pricing error is a priced risk factor that contains incremental information about stock returns of day t, and not beyond. We conclude that implied spot prices from stock futures market are useful for traders to profit in the spot market

Multiple Analytical Approaches Reveal Distinct Gene-Environment Interactions in Smokers and Non Smokers in Lung Cancer

Complex disease such as cancer results from interactions of multiple genetic and environmental factors. Studying these factors singularly cannot explain the underlying pathogenetic mechanism of the disease. Multi-analytical approach, including logistic regression (LR), classification and regression tree (CART) and multifactor dimensionality reduction (MDR), was applied in 188 lung cancer cases and 290 controls to explore high order interactions among xenobiotic metabolizing genes and environmental risk factors. Smoking was identified as the predominant risk factor by all three analytical approaches. Individually, CYP1A1*2A polymorphism was significantly associated with increased lung cancer risk (OR = 1.69;95%CI = 1.11–2.59,p = 0.01), whereas EPHX1 Tyr113His and SULT1A1 Arg213His conferred reduced risk (OR = 0.40;95%CI = 0.25–0.65,p<0.001 and OR = 0.51;95%CI = 0.33–0.78,p = 0.002 respectively). In smokers, EPHX1 Tyr113His and SULT1A1 Arg213His polymorphisms reduced the risk of lung cancer, whereas CYP1A1*2A, CYP1A1*2C and GSTP1 Ile105Val imparted increased risk in non-smokers only. While exploring non-linear interactions through CART analysis, smokers carrying the combination of EPHX1 113TC (Tyr/His), SULT1A1 213GG (Arg/Arg) or AA (His/His) and GSTM1 null genotypes showed the highest risk for lung cancer (OR = 3.73;95%CI = 1.33–10.55,p = 0.006), whereas combined effect of CYP1A1*2A 6235CC or TC, SULT1A1 213GG (Arg/Arg) and betel quid chewing showed maximum risk in non-smokers (OR = 2.93;95%CI = 1.15–7.51,p = 0.01). MDR analysis identified two distinct predictor models for the risk of lung cancer in smokers (tobacco chewing, EPHX1 Tyr113His, and SULT1A1 Arg213His) and non-smokers (CYP1A1*2A, GSTP1 Ile105Val and SULT1A1 Arg213His) with testing balance accuracy (TBA) of 0.6436 and 0.6677 respectively. Interaction entropy interpretations of MDR results showed non-additive interactions of tobacco chewing with SULT1A1 Arg213His and EPHX1 Tyr113His in smokers and SULT1A1 Arg213His with GSTP1 Ile105Val and CYP1A1*2C in nonsmokers. These results identified distinct gene-gene and gene environment interactions in smokers and non-smokers, which confirms the importance of multifactorial interaction in risk assessment of lung cancer

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery