A load identification method for the grinding damage induced stress (GDIS) distribution in silicon wafers

Zhou, Ping; Xu, Shance; Wang, Ziguang; Yan, Ying; Kang, Renke; Guo, Dongming

doi:10.1016/j.ijmachtools.2016.04.010

Cited by 33 publications

(8 citation statements)

References 25 publications

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“…SSD is detrimental to the performance and lifetime of the final part made of a silicon wafer. Zhou et al (2016) found that residual stresses resulted in warpage which increased the possibility of the wafer breakage and failure. Zhang et al (2018) suggested that the phase transformation in silicon were intolerable to the application of the integrated circuits and photovoltaics; Yin et al…”

Section: Introductionmentioning

confidence: 99%

“…Currently, there are two types of the methods for detecting SSD, namely, destructive and nondestructive methods. In terms of the destructive methods, Gao et al (2010) used the angle J o u r n a l P r e -p r o o f polishing method to obtain depth of SSD; Yin et al (2018b) used the cross-sectioning technique to study the configuration of subsurface cracks; Zhou et al (2016) revealed the microstructure in the wafers with the transmission electron microscopy; and Sekhar et al (2018) carried out the chemical etching to elucidate the asymmetry to the depth of SSD induced in the wire sawing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Two-dimensional detection of subsurface damage in silicon wafers with polarized laser scattering

Yin

Bai

Haitjema

et al. 2020

Journal of Materials Processing Technology

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two-dimensional detection of subsurface damage in silicon wafers with polarized laser scattering

Yin

Bai

Haitjema

et al. 2020

Journal of Materials Processing Technology

View full text Add to dashboard Cite

show abstract

“…Load identification is an important research area in structural health monitoring [ 1 , 2 , 3 , 4 ]. For reliability and cost effectiveness in the design and analysis of structures, accurate identification of the location and magnitude of a load is desirable.…”

Section: Introductionmentioning

confidence: 99%

“…Load identification algorithms [ 3 , 4 , 5 , 6 , 7 ] can be divided into time domain algorithms and frequency domain algorithms. Compared to frequency domain algorithms, time domain algorithms are much more valuable.…”

Section: Introductionmentioning

confidence: 99%

Load Identification for a Cantilever Beam Based on Fiber Bragg Grating Sensors

Song

Zhang

Liang

2017

Sensors

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Load identification plays an important role in structural health monitoring, which aims at preventing structural failures. In order to identify load for linear systems and nonlinear systems, this paper presents methods to identify load for a cantilever beam based on dynamic strain measurement by Fiber Bragg Grating (FBG) sensors. For linear systems, the proposed inverse method consists of Kalman filter with no load terms and a linear estimator. For nonlinear systems, the proposed inverse method consists of cubature Kalman filter (CKF) with no load terms and a nonlinear estimator. In the process of load identification, the state equations of the beam structures are constructed by using the finite element method (FEM). Kalman filter or CKF is used to suppress noise. The residual innovation sequences, gain matrix, and innovation covariance generated by Kalman filter or CKF are used to identify a load. To prove the effectiveness of the proposed method, numerical simulations and experiments of the beam structures are employed and the results show that the method has an excellent performance.

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“…In summary, input forces estimation algorithms [ 2 , 3 , 4 , 5 , 6 , 7 ] can be divided into time domain algorithms and frequency domain algorithms. Time domain algorithms are much more valuable than frequency domain algorithms, as time domain algorithms can accomplish estimation in real-time.…”

Section: Introductionmentioning

confidence: 99%

Input Forces Estimation for Nonlinear Systems by Applying a Square-Root Cubature Kalman Filter

2017

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This work presents a novel inverse algorithm to estimate time-varying input forces in nonlinear beam systems. With the system parameters determined, the input forces can be estimated in real-time from dynamic responses, which can be used for structural health monitoring. In the process of input forces estimation, the Runge-Kutta fourth-order algorithm was employed to discretize the state equations; a square-root cubature Kalman filter (SRCKF) was employed to suppress white noise; the residual innovation sequences, a priori state estimate, gain matrix, and innovation covariance generated by SRCKF were employed to estimate the magnitude and location of input forces by using a nonlinear estimator. The nonlinear estimator was based on the least squares method. Numerical simulations of a large deflection beam and an experiment of a linear beam constrained by a nonlinear spring were employed. The results demonstrated accuracy of the nonlinear algorithm.

show abstract

A load identification method for the grinding damage induced stress (GDIS) distribution in silicon wafers

Cited by 33 publications

References 25 publications

Two-dimensional detection of subsurface damage in silicon wafers with polarized laser scattering

Two-dimensional detection of subsurface damage in silicon wafers with polarized laser scattering

Load Identification for a Cantilever Beam Based on Fiber Bragg Grating Sensors

Input Forces Estimation for Nonlinear Systems by Applying a Square-Root Cubature Kalman Filter

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