Proposed Yield Model Research Articles

Interpolation-based anisotropic yield and hardening models

In this paper, an interpolation-based anisotropic yield model and a corresponding anisotropic hardening model are proposed. The proposed yield model is an improvement on Vegter and van den Boogaard (2006)'s yield model. By introducing shape parameters into Bézier basis function to control the curvature of the curve, the number of interpolation curve segments is effectively reduced. The proposed yield model has very good material applicability (suitable for BCC, FCC, HCP materials) and can directly generate the tricomponent yield surface without any numerical fitting. In addition to being applied to capture tension-compression asymmetry, the model can also be extended to describe non-orthotropy, which is usually not realized by existing yield criteria. Based on the proposed yield model, a hardening model based on characteristic yield points tracking is proposed. The so-called characteristic yield points correspond to the uniaxial loading and equi-biaxial loading yield points for constructing the yield surface. By tracking the evolution curve of yield data (stress and r-value) with specific variables (e.g. equivalent plastic strain), the yield surface at different variable levels can be continuously predicted. The proposed hardening model can not only predict the evolution of yield surface with equivalent plastic strain under proportional loading, but also predict the effects of different pre-strains and preloading paths on yield surface, and can also couple other variables (e.g. strain rate) to comprehensively consider the effects of multiple factors.

A Wafer Map Yield Prediction Based on Machine Learning for Productivity Enhancement

Manufacturing productivity in the semiconductor industry is a key factor in determining the competitiveness of manufacturers. In order to enhance productivity, evaluating the productivity of wafer maps prior to production and optimizing the productivity of wafer maps is one of the most effective solutions. The productivity of a wafer map is evaluated in advance by considering various factors affecting wafer productivity such as: gross dies, shot counts, lithography throughputs, mask field occupancy (MFO), prices, etc. Manufacturing process information is not determined at the initial wafer map design stage. Predicting the yield of new wafer maps before fabrication is a difficult challenge due to lack of process information. However, a yield prediction model is required to precisely evaluate the productivity of new wafer maps, because the yield is directly related to the productivity and the design of wafer map affects the yield. In this paper, we propose a novel yield prediction model based on deep learning algorithms. Our approach exploits spatial relationships among positions of dies on a wafer and die-level yield variations collected from a wafer test without process parameters. By modeling these spatial features, the accuracy of yield prediction significantly increased. Furthermore, experimental results showed that the proposed yield model and approach helps to design wafer maps with up to 8.59% higher productivity.

Modeling of corn yield in Brazil as a function of meteorological conditions and technological level

Abstract: The objective of this work was to develop and evaluate a method for estimating corn yield using a minimum number of parameters and limited information about crop management. The proposed method estimates potential and attainable yields based on the technological level of the production systems and on relatively simple agrometeorological models. Corn yield was estimated for the crop seasons from 2000/2001 to 2007/2008, considering several locations and regions in Brazil, and was compared with the actual yield data from official surveys. There was a high correlation between the estimated and observed yield (0.76≤R2<0.92; p<0.01), with model efficiency (E1’) ranging from 0.45 to 0.73; mean relative error (MRE) between -0.9 and 2.4%; and mean absolute error (MAE) of less than 70 kg ha-1, depending on the technological level adopted. Based on these results, the proposed yield model can be recommended to forecast yields all over the country, contributing to make this process more precise and accurate.

Multiaxial yield surface of transversely isotropic foams: Part I—Modeling

A new yield criterion is proposed for transversely isotropic solid foams. Its derivation is based on the hypothesis that the yielding in foams is driven by the total strain energy density, rather than a completely phenomenological approach. This allows defining the yield surface with minimal number of parameters and does not require complex experiments. The general framework used leads to the introduction of new scalar measures of stress and strain (characteristic stress and strain) for transversely isotropic foams. Furthermore, the central hypothesis that the total strain energy density drives yielding in foams ascribes to the characteristic stress an analogous role of von Mises stress in metal plasticity. Unlike the overwhelming majority of yield models in literature the proposed model recognizes the tension–compression difference in yield behavior of foams through a linear mean stress term. Predictions of the proposed yield model are in excellent agreement with the results of uniaxial, biaxial and triaxial FE analyses implemented on both isotropic and transversely isotropic Kelvin foam models.

On Technological Change in Crop Yields

AbstractTechnological changes in agriculture tend to alter the mass associated with segments or components of the yield distribution as opposed to simply shifting the entire distribution upwards. We propose modeling crop yields using mixtures with embedded trend functions to account for potentially different rates of technological change in different components of the yield distribution. By doing so we can test some interesting and previously untested hypotheses about the data generating process of yields. For example: (1) is the rate of technological change equivalent across components, and (2) are the probabilities of components constant over time? Our results—technological change is not equivalent across components and probabilities tend not to have changed significantly over time—have implications for modeling yields. We find estimated conditional yield densities are quite different when unique trend functions are embedded inside the mixture components versus estimating the same mixture with detrended data. Also, we prove different rates of technological change in different components lead to nonconstant variance with respect to time (i.e., heteroscedasticity). We present two applications of the proposed yield model. The first application considers climate determinants of component membership, where our results are consistent with the literature for climate determinants of yields. The second application compares the proposed yield model to USDA's current rating methodology for area‐yield crop insurance contracts and finds the proposed model may lead to more accurate rates.

Lithography parametric yield estimation model to predict layout pattern distortions with a reduced set of lithography simulations

A lithography parametric yield estimation model is presented to evaluate the lithography distortion in a printed layout due to lithography hotspots. The aim of the proposed yield model is to provide a new metric that enables the possibility to objectively compare the lithography quality of different layout design implementations. Moreover, we propose a pattern construct classifier to reduce the set of lithography simulations necessary to estimate the litho degradation. The application of the yield model is demonstrated for different layout configurations showing that a certain degree of layout regularity improves the parametric yield and increases the number of good dies per wafer.

Constitutive modeling of orthotropic sheet metals by presenting hardening-induced anisotropy

An essential work on the constitutive modeling of rolled sheet metals is the consideration of hardening-induced anisotropy. In engineering applications, we often use testing results of four specified experiments, three uniaxial-tensions in rolling, transverse and diagonal directions and one equibiaxial-tension, to describe the anisotropic features of rolled sheet metals. In order to completely take all these experimental results, including stress-components and strain-ratios, into account in the constitutive modeling for presenting hardening-induced anisotropy, an appropriate yield model is developed. This yield model can be characterized experimentally from the offset of material yield to the end of material hardening. Since this adaptive yield model can directly represent any subsequent yielding state of rolled sheet metals without the need of an artificially defined “effective stress”, it makes the constitutive modeling simpler, clearer and more physics-based. This proposed yield model is convex from the initial yield state till the end of strain-hardening and is well-suited in implementation of finite element programs.

Modeling of integrated circuit yield loss mechanisms

A yield model suited for application in a yield control system and based on in-line inspections of control wafers containing the corresponding test structures has been proposed. It is shown that the proposed yield model and yield control system can be used for modeling yield loss mechanisms and predicting efficient investments which are required in order to ensure a competitive yield of integrated circuits. An approach for the extraction of chip critical areas associated with the corresponding yield loss mechanism has been described.