Tensile and high cycle fatigue tests of NiCo thin films

Park, Jun Hyub; Chun, Yun Bae; Kim, Yun Jae; Huh, Yong-Hak; Kang, Duck Hee

doi:10.1016/j.proeng.2011.04.217

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…Gold has a lower resistivity than NiCo, thus reducing the heat generated by the probe. In this study, we used a Ni-Co alloy composed of 66.2% Ni and 33.8% Co [21]. The NiCo probe had a Young's modulus of 200 GPa, a Poisson's ratio of 0.31, and yield strength of 1876.6 MPa.…”

Section: Numerical Modeling and Analysismentioning

confidence: 99%

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Design of New Au–NiCo MEMS Vertical Probe for Fine-Pitch Wafer-Level Probing

Choa

2021

Crystals

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As fine-pitch 3D wafer-level packaging becomes more popular in semiconductor industries, wafer-level prebond testing of various interconnect structures has become increasingly challenging. Additionally, improving the current-carrying capacity (CCC) and minimizing damage to the probe and micro-interconnect structures are very important issues in wafer-level testing. In this study, we propose an Au–NiCo MEMS vertical probe with an enhanced CCC to efficiently reduce the damage to the probe and various interconnect structures, including a solder ball, Cu pillar microbump, and TSV. The Au–NiCo probe has an Au layer inside the NiCo and an Au layer outside the surface of the NiCo probe to reduce resistivity and contact stress. The current-carrying capacity, contact stress, and deformation behavior of the probe and various interconnect structures were evaluated using numerical analyses. The Au–NiCo probe had a 150% higher CCC than the conventional NiCo probe. The maximum allowable current capacity of the 5000 µm-long Au–NiCo probe was 750 mA. The Au–NiCo probe exhibited less contact force and stress than the NiCo probe. The Au–NiCo probe also produced less deformation of various interconnect structures. These results indicate that the proposed Au–NiCo probe will be a prospective candidate for advanced wafer-level testing, with better probing efficiency and higher test yield and reliability than the conventional vertical probe.

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Section: Numerical Modeling and Analysismentioning

confidence: 99%

“…Table 2 shows the mechanical properties of the materials used in the numerical simulation. We used typical material property values referenced in several studies [21][22][23]. During contact, there was friction at the interface between the probe tip and the interconnect structure.…”

Section: Numerical Modeling and Analysismentioning

confidence: 99%

Design of New Au–NiCo MEMS Vertical Probe for Fine-Pitch Wafer-Level Probing

Choa

2021

Crystals

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“…In general, manufacturing methods are known to affect the mechanical properties of materials, especially for thin films [8,9]. Thus, it is essential to perform mechanical (tensile and fatigue) tests to obtain relevant mechanical properties [10,11]. Therefore, authors reported the tensile and fatigue characteristics of the film the 2 of 11 copper film coated by Sn used in electronic devices such as LCD.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Tensile and Fatigue Properties of Copper Thin Film Depending on Process Method

2020

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In this study, tensile and fatigue tests were performed and analyzed to evaluate an influence of fabrication method on mechanical characteristics of copper thin film which widely used in flexible printed circuit board (FPCB). In general, manufacturing methods are known to affect the mechanical properties of materials, especially for thin films. The copper thin film is manufactured by a rolling process or an electrodeposition process. Therefore, specimen for tensile and fatigue tests were fabricated using by etching process with the rolled and electrodeposited thin films. First, the tensile tests were performed to obtain the elastic modulus, 0.2% offset yield stress, and tensile strength of the rolled copper and the electrodeposited copper thin film. Second, the copper thin films in FPCB is most often subjected to mechanical or/and thermal cyclic loading. The fatigue tests were performed to compare the fatigue characteristics and to evaluate an influence of fabrication method. Tensile test results showed that the elastic modulus was similar for each process, but the 0.2% yield strength and ultimate tensile strength were greater in the rolling process. In addition, the fatigue test results show that the copper thin films by the rolling process are better than by the electrodeposition process in fatigue life in all region.

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“…It is known that the mechanical properties of thin films are different from those of bulk materials. This is due to the fact that those properties are dependent on the dimensions and the fabrication process of the material [4][5][6][7]. Therefore, it is essential to carry out experiments to obtain the mechanical properties of thin films such as microelectro-mechanical system (MEMS) materials used in micromechanical structures, and flexible printed circuit board…”

Section: Introductionmentioning

confidence: 99%

Strain measurement during tensile testing using deep learning-based digital image correlation

Min

Kang

et al. 2019

Meas. Sci. Technol.

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This paper describes a novel non-contact strain measurement method defined as deep learning based on digital image correlation (DDIC). In particular, it is very difficult to measure directly displacement of gauge length during tensile testing of thin films. Therefore, we obtained the image data continuously to observe the behavior of the material during tensile testing. The sequential image data obtained at a specific position is assigned to a multi-channel input to train the deep neural network. As a result, the multi-channel image is composed of sequential images obtained along the time domain. Since these images have a correlation with each other along the time domain in each pixel, the neural network learns displacement, including temporal information. The DDIC method originates from a 3D convolutional neural network, which can extract both the spatial and the temporal domain features at the same time. A 3D convolutional filter is used as the feature extraction part of the network in order to effectively learn the input data. The deep learning is an end-to-end learning method and the deformation between two images can be measured regardless of the parameters for the nonlinear deformation of the images. An elastic modulus of 118 GPa, 0.2% yield strength of 941 MPa, ultimate tensile strength of 1108 MPa and fracture strain of 0.02414 are estimated by applying the DDIC method during a tensile test of BeCu thin film. The results of the DDIC method are compared with the displacement sensor data and digital image correlation data.

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Tensile and high cycle fatigue tests of NiCo thin films

Cited by 7 publications

References 8 publications

Design of New Au–NiCo MEMS Vertical Probe for Fine-Pitch Wafer-Level Probing

Design of New Au–NiCo MEMS Vertical Probe for Fine-Pitch Wafer-Level Probing

Comparison of Tensile and Fatigue Properties of Copper Thin Film Depending on Process Method

Strain measurement during tensile testing using deep learning-based digital image correlation

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