Void Inspection Using Stress Field Imaging in Densely Patterned Bonded Wafers

By performing atomistic calculations with a tight-binding potential, we study the displacement field induced by a screw dislocation lying along a free $\ensuremath{\langle}011\ensuremath{\rangle}$ Cu cylindrical nanowire. For this anisotropic orientation that is often encountered experimentally, we show that the displacement field ${u}_{z}$ along the nanowire can be seen as the superposition of three different fields: the screw dislocation field in an infinite medium, the warping displacement field caused by the so-called Eshelby twist, and an additional image field induced by the free surfaces. A Fourier series analysis of this latter image displacement and stress fields is given. For a circular cross section of the wire, this image field corresponds mainly to an additional warping displacement ${u}_{z}\ensuremath{\propto}xy$. The dissociation mechanism of the dislocation into partials and the surface stress effects being also captured in our simulations, the present study enables one to quantify the various contributions to the formation of the x-ray diffractograms.

Pressure alters the physical, chemical, and electronic properties of matter. The diamond anvil cell enables tabletop experiments to investigate a diverse landscape of high-pressure phenomena. Here, we introduce and use a nanoscale sensing platform that integrates nitrogen-vacancy (NV) color centers directly into the culet of diamond anvils. We demonstrate the versatility of this platform by performing diffraction-limited imaging of both stress fields and magnetism as a function of pressure and temperature. We quantify all normal and shear stress components and demonstrate vector magnetic field imaging, enabling measurement of the pressure-driven [Formula: see text] phase transition in iron and the complex pressure-temperature phase diagram of gadolinium. A complementary NV-sensing modality using noise spectroscopy enables the characterization of phase transitions even in the absence of static magnetic signatures.

A high-resolution scanning phase-measuring acoustic microscope (SPAM) has been developed and used to image the near-surface residual stress field around features etched in sputtered alumina via the acoustoelastic effect. This microscope operates at 670 MHz and has a resolution of 5–10 μm, depending upon the amount of defocus. Relative velocity changes of sample surface waves as small as 50 ppm are resolved. Images of the stress field at the tip of a 400-μm-wide slot etched in alumina are presented and compared with a finite element simulation. The SPAM uses an unconventional acoustic lens with an anisotropic illumination pattern which can measure anisotropic effects and map residual stress fields with several μm resolution and a stress sensitivity of 1/3 MPa in an alumina film.

The present work presents using scattered light technique for determination of 3D stress field. It is a generalization of the earlier introduced isodynes method, where by help of isodyne image and equilibrium equation stress field in the 2D case can be established. The 3D methods base on measurements of the intensity of the scattered light in some perpendicular to laser beam directions. It allow us to determine stress tensor but without hydrostatic component. However, this component can be obtained when two beams propagating in opposite directions interfere.

We employ third-harmonic generation (THG) imaging for noninvasive characterization of silicon wafers and microchips and demonstrate that a much higher contrast can be achieved in THG compared to reflection imaging. In particular, the THG signal clearly distinguishes between n-type and p-type silicon samples coated with native silicon dioxide, which were indistinguishable in the reflection imaging mode. The THG response showed a higher contrast in mechanically stressed samples and under in-plane electric fields. Our experimental results, supported by first-principles calculations, demonstrate that THG imaging is a robust tool for assessing doping, mechanical stress, and electric fields in silicon-based structures, offering significant potential for advanced semiconductor diagnostics and the development of next-generation electronic components.

A novel image‐driven deep learning approach embedded with model‐data‐knowledge information can directly predict the full field stress distribution and mechanical properties of composites solely based on initial scanning geometry images. The fiber spatial distribution and morphological features are identified by Scanning Electron Microscopy (SEM) initially. An effective random fiber generation algorithm is further utilized to generate images database comprising equivalent geometric models of various microstructures. Subsequently, the geometry model images database is analyzed by fast Fourier transform (FFT) to produce the database including the elastic modulus parameters and full field stress distributions of composites with distinct microstructures. Finally, an innovative image‐learning comprehensive paradigm uniting convolutional neural networks and convolutional autoencoders is elaborated systematically to learn the inherent laws of geometry images and field images. The results show that the proposed method have capacity to effectively and accurately predict the elastic modulus and full field stress distributions combined with error analysis even for stochastic boundary composites. The proposed methodology is a symbiosis of cutting‐edge image learning and non‐destructive testing techniques for composites, which can directly use the local non‐destructive or scanning images of composite structural components to quickly obtain field information and further predict damage evolution online.Highlights Proposing a novel deep learning approach combined with FFT directly to predict stress distribution of stochastic boundary composites solely based on micro‐images. Adopting equivalent geometric models dispersed by higher pixels mesh density considering extreme thin interface. Incorporate adaptive algorithms for streamlined training optimization. An innovative integration of deep learning and non‐destructive testing technology for the stress distribution and properties prediction online.

Integrated photoelasticity for nondestructive residual stress measurement in glass

<div class="htmlview paragraph">The cylinder block of a high-speed marine diesel engine is a complex structure subjected to a complex loading. The design optimization of the cylinder block requires a reliable Finite Element Model (FEM), capable to predict, with a reasonable accuracy, the actual strains and stresses.</div> <div class="htmlview paragraph">The experimental investigation presented in the paper is meant to provide the necessary information for a better estimation of the boundary conditions and the validation of the FEM of the cylinder block. In order to obtain an image of the stress field in the cylinder block, a system of 10 strain gauge rosettes have been placed at significant locations on the cylinder block. The temperature at the location of the rosettes was measured with an optical pyrometer and a method has been developed to calculate this temperature using the measured strain. A fairly good agreement was obtained between the measured and the calculated temperatures during the cooling of the engine. The measured stresses during steady state operation of the engine show a good correlation with the variation of the forces acting on the main bearings of the crankshaft.</div>

The stiffness of soils depends on the state of stress. Therefore, the velocity of wave propagation can be used to assess the state of stress and its changes. Indeed, given sufficient data, measurements can be inverted to produce a tomographic image of the stress field. A preliminary study of this approach was conducted with simulated data and is summarized briefly. Results of an experimental study are then presented. Three common geotechnical systems were tested: a footing, a moving surface load, and a retaining wall. Calculated velocity changes are compared with predicted velocity changes, with stress changes being estimated by classical analytical solutions. Good agreement is found in the three cases. The need for accuracy in travel-time measurements, difficulties in tomographic imaging, and the propagation of uncertainty from inverted velocities to stresses are discussed. Key words : geophysical methods, tomography, wave propagation, stress, retaining wall, footings.

Assessment of carbon dioxide plume behaviour within the storage reservoir and the lower caprock around the KB-502 injection well at In Salah