A simple soft lithographic nanopatterning of gold on gallium arsenide via galvanic displacement

Nanoscale patterning of gold layers on GaAs substrate is demonstrated using a combination of soft lithographic molding and galvanic displacement deposition. First, an electroless deposition method has been developed to plate gold on GaAs with ease and cost-effectiveness. The electroless metallization process is performed by dipping the GaAs substrates into a gold salt solution without any reducing agents or additives. The deposition proceeds via galvanic displacement in which gold ions in the aqueous solution are reduced by electrons arising from the GaAs substrate itself. The deposition rate, surface morphology and adhesion property can be modulated by the plating parameters such as the choice of acids and the immersion time. Second, soft lithographic patterning of nanodots, nanorings, and nanolines are demonstrated on GaAs substrates with hard-polydimethylsiloxane (h-PDMS) mold and plasma etching. This method can be easily applied to the metallization and nanopatterning of gold on GaAs surfaces.     

Model‐Reference Adaptive Moment Control of Uncertain Nonlinear Stochastic Systems

In this paper, a new model‐reference adaptive moment control method is proposed to control the first and second moments of an uncertain nonlinear system with additive external stochastic excitation. This method has established a closed‐loop control system that calculates an adaptive stochastic nonlinear input by introducing a Lyapunov function and adaptive update law. The proposed adaptive structure is innovative in trying to minimize two errors simultaneously: the moments tracking error and the error between the nonlinear system output and reference model. Furthermore, the proposed method can control the expected and covariance matrices of the states without needing to solve the complicated Fokker‐Planck‐Kolmogorov differential equation or using the approximate methods. Simulation has been performed on two practical examples, which show a good performance for the designed controller.     

A robust hybrid artificial neural network double frontier data envelopment analysis approach for assessing sustainability of power plants under uncertainty

To assess sustainability of power plants, this paper presents a novel hybrid method. To this end, self‐organizing map method of artificial neural networks is employed. Then, a double frontier data envelopment analysis is developed to rank power plants in each cluster of decision‐making units. Because outputs of power plants might be uncertain, a robust optimization approach is incorporated into proposed double frontier data envelopment analysis model to present ranks that are robust against different uncertainties. A case study is given to validate the proposed model. The case study shows that the proposed model can present improvement solutions that guide power plants towards efficient frontier and far from inefficient frontier. Given the results, decision makers can decide on which power plants should be closed and which power plants should be expanded.     

Gate Oxide Short Defect Model in FinFETs

FinFET technology is one of the most promising candidates in replacing planar MOSFET beyond the 22 nm technology node. However, the complexity of FinFET manufacturing process has caused challenges in reliable device testing. Gate oxide short (GOS) is one of the dominant defects that has significant impact on circuit reliability. In this paper, we present a GOS defect model for FinFETs by introducing the defect as a pinhole in the gate oxide of a triangular fin shape structure. The pinholes are represented by small cuboid cuts of various sizes on the fin top and sidewalls along the channel. The 3D Sentaurus TCAD simulation results in the development of an analytical GOS defect model that can be used in circuit-level fault modeling, which leads to generating more realistic test patterns.     

An Improved Estimation of the Angstrom–Prescott Radiation Coefficients for the FAO56 Penman–Monteith Evapotranspiration Method

The FAO56 Penman–Monteith (FAO56-PM) method is known as the standard method for estimating reference evapotranspiration (ET₀) in a variety of climate types. Global solar radiation (R_s) is one of the essential inputs of this model, which is usually estimated from the Angstrom–Prescott (AP) method. The major drawback of the FAO56 pre-defined AP coefficients application is that the AP coefficients might need local calibration, to estimate ET₀ accurately. The aim of this study is to compare the effect of the FAO56 pre-defined AP coefficients (i.e. a and b) and the locally calibrated ones, on estimating daily ET₀ in 15 sites over Iran. Using long-term (1980–2007) experimental global solar radiation data (R_s), new locally calibrated (a) and (b) coefficients are suggested and new ET₀ values are determined accordingly. It was found that the range of the calibrated AP coefficients (a, b) are climate dependent and locally different from those of recommended by the FAO56-PM method. Estimated ET₀ at daily scale, improved up to 72.7 % when the calibrated AP coefficients were applied instead of FAO56 pre-defined AP coefficients. Based on the results, applying the FAO56 pre-defined AP coefficients (i.e. a?=?0.25 and b?=?0.50) in northern subtropical-humid and southern hot climates caused larger ET₀ errors. By contrast, the least ET₀ errors were found in cool arid and cool semi-arid inland climates, locating about 1,330 above sea level. The correlations between the calibrated AP coefficients and geographical factors are also discussed in this research.