Novel yield model for integrated circuits with clustered defects

As wafer sizes increase, the clustering phenomenon of defects increases. Clustered defects cause the conventional Poisson yield model underestimate actual wafer yield, as defects are no longer uniformly distributed over a wafer. Although some yield models, such as negative binomial or compound Poisson models, consider the effects of defect clustering on yield prediction, these models have some drawbacks. This study presents a novel yield model that employs General Regression Neural Network (GRNN) to predict wafer yield for integrated circuits (IC) with clustered defects. The proposed method utilizes five relevant variables as input for the GRNN yield model. A simulated case is applied to demonstrate the effectiveness of the proposed model.     

Use of vegetation index and meteorological parameters for the prediction of crop yield in India

Monsoon rainfall distribution over the Indian sub‐continent is inconsistent every year. Due to uncertainty and dependence on the monsoon onset and weather conditions, estimation of crop yield in India is difficult. In this paper, analyses of the crop yield, normalized difference vegetation index, soil moisture, surface temperature and rainfall data for 16 years (from 1984 to 1999) have been carried out. A non‐linear iterative multivariate optimization approach (quasi‐Newton method with least square loss function) has been used to derive an empirical piecewise linear crop yield prediction equation (with a break point). The derived empirical equation (based on 1984 to 1998 data) has been used to predict 1999 crop yield with R²>0.90. The model has been validated for the three years 1997, 1998 and 1999. A crop yield prediction equation has been obtained for each province in India (for wheat and rice) that accounts for>90% of the variance in the dataset.     

Using site-specific data to estimate energy crop yield

The estimation of energy crop yields is important, to help the firms responsible for collecting them to estimate biomass production in a given area, for example. A Bayesian modelling framework for site-specific yield estimation is presented in this paper. The proposed approach is based on a hierarchical model describing between-site and within-site yield variability. Probability distributions are used to describe the uncertainty of model estimations. The model can be fitted to site-specific yield data, to obtain both average and site-specific yield estimates. Site-specific yield data may be obtained from measurements for crop species other than those for which estimations are required, or from past measurements on perennial crop species grown over a period of several years at a given site. These two options were illustrated in two case studies, in which our model was used to estimate the yields of several energy crops. In most situations, site-specific yield estimations were more accurate than average estimations.     

Fast prediction and optimization of yield in gallium arsenide large-signal MMICs

A study on the effects of the geometrical and physical parameters of the GaAs MMIC process on the yield of large-signal circuits is presented. Large-signal yield analysis as well as large-signal yield optimization are performed using a large-signal lumped-element MESFET model related to MMIC process parameters, and suitable for implementation in commercial microwave CAD tools. The characterization of all the statistical variables of a large-signal circuit provides a better understanding of the yield behavior. In particular, the sensitivity of large-signal yield to MMIC process parameters is computed and the statistical behavior of each parameter is presented by means of yield sensitivity histograms. © 1998 John Wiley & Sons, Inc. Int J RF and Microwave CAE 8: 68–76, 1998.     

Towards an operational system for regional-scale rice yield estimation using a time-series of Radarsat ScanSAR images

This paper demonstrates that Radarsat ScanSAR data can be an important data source of radar remote sensing for monitoring crop systems and estimation of rice yield in large areas in tropical and sub-tropical regions. Experiments were carried out to show the effectiveness of Radarsat ScanSAR data for rice yield estimation in the whole province of Guangdong, South China. A methodology was developed to deal with a series of issues in extracting rice information from the ScanSAR data, such as topographic influences, levels of agro-management, irregular distribution of paddy fields and different rice cropping systems. A model was provided for rice yield estimation based on the relationship between the backscatter coefficient of multi-temporal SAR data and the biomass of rice. The study indicates that the whole procedure can become a low-cost and convenient operational system for large-scale rice yield estimation which is difficult for conventional methods.     

Numerical yield line analysis

A numerical method based on the virtual work approach of the yield line theory is presented. The method consists of computing the yield load of a plate based on the geometry of an assumed collapse mechanism defined by means of nodes, planes and lines. Since the method is numerical, it allows the yield line analysis of plates with complex shapes, assumed mechanisms and loadings. Algorithms for the calculations of the work done by the external loads on the plate and the internal work dissipated by the yield lines in the assumed mechanism are described. The features of a computer program are outlined, and a numerical example of the numerical yield line analysis of a reinforced concrete slab is given.     

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.     

Elasto-plastic analysis of a plane stress problem using linearized yield surface and mixed hardening rule

The objective of this paper is to present the mathematical formulations in the incremental theory of plasticity, which is based on the mixed hardening rule and a linear yield surface. A three-parameter, uniaxial symmetric, linear yield surface suitable for tension-weak as well as equal tension and compression yield stress material is presented. This yield condition, along with the mixed hardening and associated flow rules is used to formulate the constitutive laws for sides and corners of the yield surface. The formulation is based on incremental plasticity with the assumption of small displacements and is suitable for plane stress problems under monotonie and cyclic loading.

The mixed hardening rule, which is mathematically modeled, could be changed to either kinematic or isotropic hardening by a simple change in the model. This hardening rule could handle different degrees of Bauschinger effect, as opposed to kinematic hardening, which assumes only an ideal Bauschinger effect, or isotropic hardening, which does not account for the effect at all. The theory is applied to a ductile material using the finite element method and cyclic loading.     

Combining negative binomial and Weibull distributions for yield and reliability prediction

A key productivity metric in semiconductor manufacturing is wafer test yield - the fraction of dies deemed functional following wafer probe testing. Wafer test yield is directly related to semiconductor manufacturing profitability: The higher the yield, the lower the cost of producing a functional chip, and therefore the greater the potential profit. Because wafer test yield is such a critical variable in a products profit potential, accurate yield projection models are essential to semiconductor manufacturers economic success. It is important to understand the correlation between defects causing yield loss and defects causing reliability failures. This article presents a modeling methodology and supporting data, demonstrating that yield and reliability defects can be directly linked in a unified model.     

A yield-enhanced global optimization methodology for analog circuit based on extreme value theory

The challenge in variation-aware circuit optimization with consideration of yield is the trade-off between optimized performance, yield and optimization runtime. This paper presents a practical variationaware circuit global optimization framework named GOYE, which shows the advantages on performance, yield and runtime. It uses an approach called constraint violation elimination (CVE) in global search phase to prune initial starting points and uses the gradient-based method in local search to locate optimum. The worst-case analysis (WCA), which is necessary for variation-aware circuit optimization, is nested in the local optimization process. The efficiency is significantly improved by a novel method based on extreme value theory (EVT). Our EVT-based method is also the first one that allows users to control the target yield such that under-design or over-design can be avoided. A design example in TSMC 65 nm technology is illustrated in the paper where all performance achieves three-sigma yield with consideration of environmental and inter-die/intra-die process variations.     

Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data

Wheat is one of the most important crops in Hungary, which represents approximately 20% of the entire agricultural area of the country, and about 40% of cereals. A robust yield method has been improved for estimating and forecasting wheat yield in Hungary in the period of 2003–2015 using normalized difference vegetation index (NDVI) derived from the data of the Moderate Resolution Imaging Spectroradiometer. Estimation was made at the end of June – it is generally the beginning of harvest of winter wheat in Hungary – while the forecasts were performed 1–7 weeks earlier. General yield unified robust reference index (GYURRI) vegetation index was calculated each year using different curve-fitting methods to the NDVI time series. The correlation between GYURRI and country level yield data gave correlation coefficient (r) of 0.985 for the examined 13 years in the case of estimation. Simulating a quasi-operative yield estimation process, 10 years’ (2006–2015) yield data was estimated. The differences between the estimated and actual yield data provided by the Hungarian Central Statistical Office were less than 5%, the average difference was 2.5%. In the case of forecasting, these average differences calculated approximately 2 and 4 weeks before the beginning of harvest season were 4.5% and 6.8%, respectively. We also tested the yield estimation procedure for smaller areas, for the 19 counties (Nomenclature of Territorial Units for Statistics-3 level) of Hungary. We found that, the relationship between GYURRI and the county level yield data had r of 0.894 for the years 2003–2014, and by simulating the quasi-operative forecast for 2015, the resulting 19 county average yield values differed from the actual yield as much as 8.7% in average.     

Crop yield estimation by satellite remote sensing

Two methods for estimating the yield of different crops in Hungary from satellite remote sensing data are presented. The steps of preprocessing the remote sensing data (for geometric, radiometric, atmospheric and cloud scattering correction) are described. In the first method developed for field level estimation, reference crop fields were selected by using Landsat Thematic Mapper (TM) data for classification. A new vegetation index (General Yield Unified Reference Index (GYURI)) was deduced using a fitted double-Gaussian curve to the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data during the vegetation period. The correlation between GYURI and the field level yield data for corn for three years was R ²=0.75. The county-average yield data showed higher correlation (R ²=0.93). A significant distortion from the model gave information of the possible stress of the field. The second method presented uses only NOAA AVHRR and officially reported county-level yield data. The county-level yield data and the deduced vegetation index, GYURRI, were investigated for eight different crops for eight years. The obtained correlation was high (R ²=84.6–87.2). The developed robust method proved to be stable and accurate for operational use for county-, region- and country-level yield estimation. The method is simple and inexpensive for application in developing countries, too.     

Large-area relation of landsat MSS and NOAA-6 AVHRR spectral data to wheat yields

Landsat MSS data transformed into Kauth-Thomas greenness were averaged over 5 n.mi. × 6 n.mi. sample segments from the U.S. Great Plains winter and spring wheat (Triticum aestivum) regions, and related by regression analysis to yields reported by county, crop reporting district (crd), and state levels. Evidence of a linear relation between winter- and spring-wheat yields and Landsat spectral data at a broad scale is shown for 1978 and 1979. A common slope of about 1.6 (Bu/A)/unit greenness is discerned for the relation between yield and spectral greenness. Tests at both a smaller scale on sets of field-level spectral data and yield and at a large scale on 25 mi. × 25 mi. gridded spectral data from the NOAA-6 avhrr sensor support the relation. The implications of these results to yield estimation from satellite spectral data are discussed.     

Comments on "Optimum quantization in dynamic systems"

A proposed algorithm for determining the optimal parameters of a quantizer imbedded in a closed-loop dynamical system is shown to yield incorrect results. A counterexample to the algorithm is presented, as well as a correct interpretation of this result.     

An iterative method for plastic analysis of frames

A numerical approach for the computation of yield and plastic collapse of framed structures is described. Based on the ‘initial stress’ method in which the global matrix is formed once only, the algorithm differs from other computer approaches in that moments in excess of their yield values are redistributed using ‘constant stiffness’ iterations. Under a given set of external loads, convergence of the numerical process gives a solution in which equilibrium is satisfied without any violation of yield at the joints. Collapse of a structure is indicated by a sudden increase in the number of iterations required for convergence, and correspondingly large nodal displacements. Examples are presented in which both loading and unloading cycles are followed, leading to demonstrations of elastic shake down and incremental collapse Comparisons with solutions obtained by more traditional methods suggest that the proposed algorithm represents an accurate and versatile approach to the analysis of yield in framed structures.     

Application of Meteosat derived meteorological information for crop yield predictions in Europe

This paper presents the utilization of surface fluxes and relative evapotranspiration derived from satellites for crop yield prediction using a dedicated crop growth simulation algorithm, the Environmental Analysis and Remote Sensing (EARS) Crop Growth Simulation algorithm (EARS-CGS). The objective was to test the EARS-CGS algorithm independent of ground data for crop yield prediction at national level in Europe. The algorithm is based on existing crop yield models but has been modified to assimilate satellite derived global solar radiation and actual evaporation information. The algorithm simulates crop biomass. A statistical method is utilized to relate crop biomass to crop yield and to correct for regional differences in yields that are not the result of radiation or water limitation. Six years of Meteosat data were processed to predict winter wheat and spring barley yields for Spain and the UK. The predicted yields were compared to the national reported yields and to forecasts of the European Statistical Office (EUROSTAT) and the Monitoring Agriculture by Remote Sensing-Crop Growth Monitoring System (MARS-CGMS). To evaluate the timeliness of the predictions the reported yields were compared to yield predictions made at different stages of the growing season. The results presented in this paper demonstrate that crop yields predicted from meteorological satellites can be applied to provide timely and reliable crop yield forecasts.     

Optimization with trajectory sensitivity considerations

A new algorithm is presented for reducing trajectory sensitivity for the optimal linear regulator problem. Results for a first-order example yield improvements over existing methods.     

基于复形法模拟与优化硫代磷酰氯的连续化生产反应器

针对O,O-二甲基硫代磷酰氯传统的分批法生产过程,生产能力小、产品质量与收率不稳定等缺点,提出了多级循环管式反应器串联的连续化生产新工艺.以最大总收率为优化目标,利用MATLAB编程,采用复形法优化各级循环管式反应器内的加碱量.结果表明:各级反应器内加碱分布逐级减少,增加串联级数与降低反应温度,可以提高O,O-二甲基硫代磷酰氯的收率及产物质量.建立的单级循环管式反应器连续化生产的中试装置表明:实验与模拟计算结果较吻合.为建立大规模连续化生产装置与优化,提供理论依据.     

Determination of the locations and capacities of sugar cane loading stations in Thailand

In this paper, we address the problem of the location of sugar cane loading stations in Thailand. A loading station is a facility for collecting cane from small farmers; the cane is then transported to a sugar mill by a large truck. An improperly located loading station can result in high investment and transportation costs in the sugar industry. A mathematical model and a heuristic algorithm were developed to determine the suitable capacity of existing loading stations, the locations and capacities of new loading stations and the allocations of cane field harvests to each loading station. The model accounted for variations in the cane yield of each field during the harvesting periods and between crop years. The objective function was the minimization of the associated costs, including the investment costs, the transportation costs and the cost of the cane yield loss if the cane is not harvested at an optimal time. The performance of the developed heuristics was assessed under various scenarios. The results were shown to deviate slightly from the solution to the mathematical model. The sensitivities of the solutions under variations of the transportation cost, yield loss cost and investment costs were studied. The model was also applied to an industrial case study. A relevant and accurate solution was obtained.