Determination of lapping parameters for silicon wafer using an artificial neural network

Öztürk, Savaş; Kayabaşı, Erhan; Çelik, Erdal

doi:10.1007/s10854-017-7912-4

Cited by 16 publications

(4 citation statements)

References 25 publications

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“…In the past few decades, many artificial intelligence technologies [17,18,19] have been applied to describe this nonlinear relationship, such as genetic algorithm, neurocomputing, fuzzy logic, and artificial neural network (ANN). Compared with other methods, artificial neural network (ANN) [20,21] is a promising technology in the performance prediction field because it can solve problems faster. ANN models [16,22,23,24,25] are well known for function approximation and feature extraction of highly complex nonlinear relationships without any physical background knowledge.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Relationship Analysis of Mechanical Properties with Mg Content and Heat Treatment Parameters in Al–7Si Alloys Using Artificial Neural Network

Zhang

Haiyang

et al. 2019

Materials

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In this paper, an artificial neural network (ANN) model with high accuracy and good generalization ability was developed to predict and optimize the mechanical properties of Al–7Si alloys. The quantitative correlation formulas of the mechanical properties with Mg content and heat treatment parameters were established based on the transfer function and weight values. The relative importance of the input variables, Mg content and heat treatment parameters, on the mechanical properties of Al–7Si alloys were identified through sensitivity analysis. The results indicated that the mechanical properties of Al–7Si alloys were sensitive to Mg content and aging temperature. Then the individual and the combined influences of these input variables on the properties of Al–7Si alloys were simulated and the process parameters were optimized using the artificial neural network model. Finally, the proposed model was validated to be a robust tool in predicting the mechanical properties of the Al–7Si alloy by conducting experiments.

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Section: Introductionmentioning

confidence: 99%

Quantitative Relationship Analysis of Mechanical Properties with Mg Content and Heat Treatment Parameters in Al–7Si Alloys Using Artificial Neural Network

Zhang

Haiyang

et al. 2019

Materials

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“…Furthermore, they derived many different parameters for varying operation conditions to minimise material consumption and surface roughness using ANNs algorithm [19]. Furthermore, the lapping parameters of silicon wafers were studied to optimise the lapping duration and minimise lapping materials consumed during lapping process [20]. Moreover, the BA-based ANN also was used to estimate Seebeck coefficient and other parameters of the thermoelectric materials [12].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence solution to extract the dielectric properties of materials at sub‐THz frequencies

Guneser¹

2019

IET Science, Measurement & Technology

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“…The roughness is often extracted from the surface morphology. More and more measuring instruments are used to get the surface morphology, such as the contact surface profiler [2], laser interferometric profiler [3], and noncontact digital optical profiler [4]. The contact surface profiler is the most commonly used instrument.…”

Section: Introductionmentioning

confidence: 99%

“…It is used to measure the surface profile. Because it is connected with the computer, the surface profile information can display on the computer [2]. When the surface morphology is obtained, the method of extracting the roughness needs to be considered.…”

Section: Introductionmentioning

confidence: 99%

The Calculation of Roughness Uncertainty by Fitting B‐Spline Filter Assessment Middle Lines

Liu

Sang

et al. 2019

Mathematical Problems in Engineering

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The roughness and uncertainty are important parameters of surface morphology. The least square middle line method is often used to estimate the roughness and its uncertainty. However, the roughness and its uncertainty obtained by the least square middle line method are inaccurate. This paper proposes a method to calculate exactly the roughness and its uncertainty by piecewise fitting the smooth B-spline filter assessment middle lines. A B-spline smoothing filter is selected to determine the assessment middle line of roughness. The B-spline filter can not only give the accurate roughness, but also obtain the smooth assessment middle line. The model of roughness uncertainty is proposed by piecewise fitting B-spline filter middle lines as the quadratic curves. The S-shaped test part is used to verify the model of roughness uncertainty.

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Determination of lapping parameters for silicon wafer using an artificial neural network

Cited by 16 publications

References 25 publications

Quantitative Relationship Analysis of Mechanical Properties with Mg Content and Heat Treatment Parameters in Al–7Si Alloys Using Artificial Neural Network

Quantitative Relationship Analysis of Mechanical Properties with Mg Content and Heat Treatment Parameters in Al–7Si Alloys Using Artificial Neural Network

Artificial intelligence solution to extract the dielectric properties of materials at sub‐THz frequencies

The Calculation of Roughness Uncertainty by Fitting B‐Spline Filter Assessment Middle Lines

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