Yield modeling of elliptical defect

Physical defects have always played an important role in integrated circuit(IC)yields,and the design sensitivity to these physical elements has continued to increase in today’s nanometer technologies.The modeling of defect out-lines that exhibit a great variety of defect shapes is usually modeled as a circle,which causes the errors of critical area estimation.Since the outlines of 70%defects approximate to elliptical shapes,a novel yield model associated with elliptical outlines of defects is presented.This model is more general than the circular defects model as the latter is only an instance of the proposed model.Comparisons of the new and circular models in the experiment show that the new model can predict yield caused by real defects more accurately than what the circular model does,which is of significance for the prediction and improvement of the yield.

Scratch-concerned yield modeling for IC manufacturing involved with a chemical mechanical polishing process

In existing integrated circuit (IC) fabrication methods,the yield is typically limited by defects generated in the manufacturing process.In fact,the yield often shows a good correlation with the type and density of the defect.As a result,an accurate defect limited yield model is essential for accurate correlation analysis and yield prediction.Since real defects exhibit a great variety of shapes,to ensure the accuracy of yield prediction,it is necessary to select the most appropriate defect model and to extract the critical area based on the defect model.Considering the realistic outline of scratches introduced by the chemical mechanical polishing (CMP) process,we propose a novel scratch-concerned yield model.A linear model is introduced to model scratches.Based on the linear model,the related critical area extraction algorithm and defect density distribution are discussed.Owing to higher correspondence with the realistic outline of scratches,the linear defect model enables a more accurate yield prediction caused by scratches and results in a more accurate total product yield prediction as compared to the traditional circular model.

The effects of data aggregation on long-term projections of forest stands development

Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories estimate forest characteristics for grid cell areas(pixels),which are then usually summarized at the stand level.Using the ALS-based high-resolution Norwegian Forest Resource Maps(16 m×16 m pixel resolution)alongside with stand-level growth and yield models,this study explores the impact of three levels of pixel aggregation(standlevel,stand-level with species strata,and pixel-level)on projected stand development.The results indicate significant differences in the projected outputs based on the aggregation level.Notably,the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation,ranging from-301 to+253 m^(3)·ha^(-1)for single stands.The differences were,on average,higher for broadleaves than for spruce and pine dominated stands,and for mixed stands and stands with higher variability than for pure and homogenous stands.In conclusion,this research underscores the critical role of input data resolution in forest planning and management,emphasizing the need for improved data collection practices to ensure sustainable forest management.

Evaluation of Sediment Yield Predicting Models of Ghana

Fluvial sediment transport data is a very important data for effective water resource management.However,acquiring this data is expensive and tedious hence sediment yield modeling has become an alternative approach in estimating river sediment yields.In Ghana,several sediment yield predicting models have been developed to estimate the sediment yields of ungauged rivers including the Pra River Basin.In this paper,10 months sediment yield data of the Pra River Basin was used to evaluate the existing sediment yield predicting models of Ghana.A regression analysis between predicted sediment yield data derived from the models and the observed suspended sediment yields of the Pra Basin was done to determine the extent of estimation of observed sediment yields.The prediction of suspended sediment yield was done for 4 out of 5 existing sediment yield predicting models in Ghana.There were variations in sediment yield between observed and predicted suspended sediments.All predicted sediment yields were lower than observed data except for equation 3 where the results were mixed.All models were found to be good estimators of fluvial sediments with the best model being equation 4.Sediment yield tends to increase with drainage basin area.

Planting density affected biomass and grain yield of maize for seed production in an arid region of Northwest China

Field experiments were conducted from 2012 to 2015 in an arid region of Northwest China to investigate the effects of planting density on plant growth, yield, and water use efficiency（WUE） of maize for seed production. Five planting densities of 6.75, 8.25, 9.75, 11.25 and 12.75 plants/m^2 were conducted in 2012, and a planting density of 14.25 plants/m^2 was added from 2013 to 2015. Through comparison with the Aqua Crop yield model, a modified model was developed to estimate the biomass accumulation and yield under different planting densities using adjustment coefficient for normalized biomass water productivity and harvest index. It was found that the modified yield model had a better performance and could generate results with higher determination coefficient and lower error. The results indicated that higher planting density increased the leaf area index and biomass accumulation, but decreased the biomass accumulation per plant. The total yield increased rapidly as planting density increased to 11.25 plants/m^2, but only a slight increase was observed when the density was greater than 11.25 plants/m^2. The WUE also reached the maximum when planting density was 11.25 plants/m^2, which was the recommended planting density of maize for seed production in Northwest China.

GROWTH AND YIELD MODELS FOR DAHURIAN LARCH PLANTATIONS

Growth and yield models were developed for individual tress and stands based on 336 temporary plots with 405 stem analysis trees of dahurian larch ( Larix gmelinii( Rupr. )Rupr.) plantations throughout Daxing'anling mountains. Several equations were selected using nonlinear regression analysis. Results showed that the Richards equation was the best model for estimating tree height, stand mean height and stand dominant height from age; The Power equation was the best model for prediction tree volume from DBH and tree height; The logarithmic stand volume equation was good for predicting stand volume from age, mean height, basal area and other stand variables. These models can be used to construct the volume table, the site index table and other forestry tables for dahurian larch plantations.

Weather Yield Model for the Semi Tropical Region (Pakistan)

Weather models are essential tools for checking of the effect of the weather elements in terms of their effect on the production of the crop. This research is an attempt to see the effect of only two variables i.e., temperature and rainfall for the division Faisalabad (semitropical region of Pakistan).The model fitted is of the linear form:the values of a,b, c have been found. The expected yield has been calculated by using the aridity indices (X1 and X2 ) and the result in the form of coefficient of determination R2 has been found equal to 0.166. The significance of the regression coefficient has been tested, which shows that the contribution to the yield from aridity index at germination and that at ripening is significant.The wheat yields are the results of a wide variety of variables, most of which show varying degree of relationship with one another, some positive and some negative in terms of output. These variables may be technology, fertilizers, pesticides, epidemics, kinds of seeds used, market price of crop and the area under cultivation etc, which can be the source of variation in the wheat yield. Since rainfall during germination and temperature at the ripening periods are the necessary factors for the yield of wheat, for this purpose these parameters have been studied in order to their contribution.

A synthetic semi-empirical physical model of secondary electron yield of metals under E-beam irradiation

Calculations of secondary electron yield（SEY） by physical formula can hardly accord with experimental results precisely. Simplified descriptions of internal electron movements in the calculation and complex surface contamination states of real sample result in notable difference between simulations and experiments. In this paper, in order to calculate SEY of metal under complicated surface state accurately, we propose a synthetic semi-empirical physical model. The processes of excitation of internal secondary electron（SE） and movement toward surface can be simulated using this model.This model also takes into account the influences of incident angle and backscattering electrons as well as the surface gas contamination. In order to describe internal electronic states accurately, the penetration coefficient of incident electron is described as a function of material atom number. Directions of internal electrons are set to be uniform in each angle. The distribution of internal SEs is proposed by considering both the integration convergence and the cascade scattering process.In addition, according to the experiment data, relationship among desorption gas quantities, sample ultimate temperature and SEY is established. Comparing with experiment results, this synthetic semi-empirical physical model can describe the SEY of metal better than former formulas, especially in the aspect of surface contaminated states. The proposed synthetic semi-empirical physical model and presented results in this paper can be helpful for further studying SE emission, and offer an available method for estimating and taking advantage of SE emission accurately.

A New Modification to Shear Lag Model as Applied to Stiffness and Yield Strength of Short Fiber Reinforced Metal Matrix Composites

A new modification for the shear lag model is given and the expressions for the stiffness and yield Strength of short fiber metal matri×composite are derived. These expressions are then compared with our experimental data in a SiCw/Al-Li T6 composite and the published experimental data on different SiCw/Al T6 composites and also compared with the previous shear lag models and the other theoretical models.

Estimating Pinus palustris tree diameter and stem volume from tree height,crown area and stand-level parameters

Accurate and efficient estimation of forest growth and live biomass is a critical element in assessing potential responses to forest management and environmental change. The objective of this study was to develop models to predict longleaf pine tree diameter at breast height （dbh） and merchantable stem volume （V） using data obtained from field measurements. We used longleaf pine tree data from 3,376 planted trees on 127 permanent plots located in the U.S. Gulf Coastal Plain region to fit equations to predict dbh and V as functions of tree height （H） and crown area （CA）. Prediction of dbh as a function of H improved when CA was added as an additional independent variable. Similarly, predic- tions of V based on H improved when CA was included. Incorporation of additional stand variables such as age, site index, dominant height, and stand density were also evaluated but resulted in only small improvements in model performance. For model testing we used data from planted and naturally-regenerated trees located inside and outside the geographic area used for model fitting. Our results suggest that the models are a robust alternative for dbh and V estimations when H and CA are known on planted stands with potential for naturally-regenerated stands, across a wide range of ages. We discuss the importance of these models for use with metrics derived from remote sensing data.

Improved mechanical properties and strengthening mechanism with the altered precipitate orientation in magnesium alloys

Aging prior to twinning deformation was proposed to alter the precipitate orientation of the plate-shapedβ-MgAlfrom(0002)basal planes(named basal plates)to■prismatic planes(named prismatic plates)in AZ31 Mg alloy.The experimental results showed that the compressive yield strength(CYS)of the sample containing prismatic plates increased 40 MPa and the compression ratio raised by 22%compared to that containing basal plates.The underlying strengthening mechanism was analyzed via a yield strengthen(YS)model with a function of grain size,precipitate characters(size,oritention,fraction)and Schmid factor(SF).It revealed that the improvement of CYS was mainly attributed to the altered precipitate orientation and refined grain size produced by twinning deformation.Particularly,the prismatic plates always have a stronger hardening effect on basal slip than basal plates under the same varites of precipitate diameter and SF.Besides,the decreased CRSS ratio of prismatic slip to basal slip revealed that the activity of non-basal slip in Mg alloy might be enhanced.More activated slip systems provided more mobile dislocations,contributing to the large compression ratio of the Mg rolled sheet with prismatic plates.

Rheological Behaviors of Fresh Cement Pastes with Polycarboxylate Superplasticizer

The rheologicalbehaviors of fresh cement paste with polycarboxylate superplasticizer were systematically investigated.Influentialfactors including superplasticizer to cement ratio（Sp/C）,water to cement ratio（w/c）,temperature,and time were discussed.Fresh cement pastes with Sp/Cs in the range of 0 to 2.0% and varied W/Cs from 0.25 to 0.5 were prepared and tested at 0,20 and 40 °C,respectively.Flowability and rheologicaltests on cement pastes were conducted to characterize the development of the rheologicalbehavior of fresh cement pastes over time.The exprimentalresults indicate that the initialflowability and flowability retention over shelf time increase with the growth in superplasticizer dosage due to the plasticizing effect and retardation effect of superplasticizer.Higher temperature usually leads to a sharper drop in initialflowability and flowability retention.However,for the cement paste with high Sp/C or w/c,the flowability is slightly affected by temperature.Yield stress and plastic viscosity show similar variation trends to the flowability under the abovementioned influentialfactors at low Sp/C.In the case of high Sp/C,yield stress and plastic viscosity start to decline over shelf time and the decreasing rate descends at elevated temperature.Moreover,two equations to roughly predict yield stress and plastic viscosity of the fresh cement pastes incorporating Sp/C,w/c,temperature and time are developed on the basis of the existing models,in which experimentalconstants can be extracted from a database created by the rheologicaltest results.

Scale effect and geometric shapes of grains

The role-of-mixture approach has become one of the widely spread ways to investigate the mechanical properties of nano-materials and nano-structures, and it is very important for the simulation results to exactly compute phase volume fractions. The nanocrystalline （NC） materials are treated as three-phase composites consisting of grain core phase, grain boundary （GB） phase and triple junction phase, and a two-dimensional three-phase mixture regular polygon model is established to investigate the scale effect of mechanical properties of NC materials due to the geometrical polyhedron characteristics of crystal grain. For different multi-sided geometrical shapes of grains, the corresponding regular polygon model is adopted to obtain more precise phase volume fractions and exactly predict the mechanical properties of NC materials.

A New Prediction Model for Grain Yield in Northeast China Based on Spring North Atlantic Oscillation and Late-Winter Bering Sea Ice Cover

Accurate estimations of grain output in the agriculturally important region of Northeast China are of great strategic significance for guaranteeing food security.New prediction models for maize and rice yields are built in this paper based on the spring North Atlantic Oscillation index and the Bering Sea ice cover index.The year-to-year increment is first forecasted and then the original yield value is obtained by adding the historical yield of the previous year.The multivariate linear prediction model of maize shows good predictive ability,with a low normalized root-mean-square error（NRMSE）of 13.9%,and the simulated yield accounts for 81%of the total variance of the observation.To improve the performance of the multivariate linear model,a combined forecasting model of rice is built by considering the weight of the predictors.The NRMSE of the model is 12.9%and the predicted rice yield explains 71%of the total variance.The corresponding cross-validation test and independent samples test further demonstrate the efficiency of the models.It is inferred that the statistical models established here by applying year-to-year increment approach could make rational prediction for the maize and rice yield in Northeast China before harvest.The present study may shed new light on yield prediction in advance by use of antecedent large-scale climate signals adequately.

Numerical modeling of an advancing hydraulically-driven pile in sand

The penetration of a model pile through sand was investigated via a numerical analysis. Data from nine triaxial compression tests on dense specimens at different stress levels was generalized and used to create an empirical non-linear plastic hardening stress-strain relation for use in the analysis. As the computer program used is capable of large displacement analyses in radial symmetry, we expected that the analysis would easily reproduce the tip resistance penetration profile of the model pile in sand of known density and stress. However, initial attempts led to over-prediction. Successful analyses required both successive reformations of the mesh and the complete elimination of the dilatant peak in soil strength, which is naturally eliminated under large confining stress directly beneath the advancing tip, and that soil in the far-field had strained insufficiently to reach peak strength. Thus, the soil around the shaft must have been sheared to a critical state as it flowed past the tip. The hypothesis that the resistance to displacement piles in sand is mainly a function of the deformability of the sand was again proven, and the use of peak strength in the traditional bearing capacity formulae was found to be inappropriate. Independent investigation in this direction is needed to quantify the hypothesis.

Comparison of three models for winter wheat yield prediction based on UAV hyperspectral images

Predicting crop yield timely can considerably accelerate agricultural production management and food policy-making,which are also important requirements for precise agricultural development.Given the development of hyperspectral imaging technology,a simple and efficient modeling method is convenient for predicting crop yield by using airborne hyperspectral images.In this study,the Unmanned Aerial Vehicle(UAV)hyperspectral and maturity yield data in 2014-2015 and 2017-2018 were collected.The winter wheat yield prediction model was established by optimizing Vegetation Indices(VIs)feature scales and sample scales,incorporating Partial Least Squares Regression(PLSR),Random Forest algorithm(RF),and Back Propagation Neural Network algorithm(BPN).Results showed that PLSR stands out as the optimal wheat yield prediction model considering stability and accuracy(RMSE=948.88 kg/hm2).Contrary to the belief that more input features result in higher accuracy,PLSR,RF,and BPN models performed best when trained with the top 3,8,and 4 VIs with the highest correlation,respectively.With an increase in training samples,model accuracy improves,reaching stability when the training samples reach 70.Using PLSR and optimal feature scales,UAV yield prediction maps were generated,holding significant value for field management in precision agriculture.

Novel serpentine structure design method considering confidence level and estimation precision

Due to the importance of metal layers in the product yield,serpentine test structures are usually fabricated on test chips to extract parameters for yield prediction.In this paper,the confidence level and estimation precision of the average defect density on metal layers are investigated to minimize the randomness of experimental results and make the measured parameters more convincing.On the basis of the Poisson yield model,the method to determine the total area of all serpentine test structures is obtained using the law of large numbers and the Lindeberg-Levy theorem.Furthermore,the method to determine an adequate area of each serpentine test structure is proposed under a specific requirement of confidence level and estimation precision.The results of Monte Carlo simulation show that the proposed method is consistent with theoretical analyses.It is also revealed by wafer experimental results that the method of designing serpentine test structure proposed in this paper has better performance.