Deep-Learning-Based Defect Evaluation of Mono-Like Cast Silicon Wafers

Solar cells based on mono-like cast silicon (MLC-Si) have been attracting increasing attention in the photovoltaic (PV) market due to their high energy conversion efficiency and low cost. As in the production of monocrystalline silicon (MC-Si) and polycrystalline silicon (PC-Si) cells, various defects will inevitably occur during the production process of MLC-Si cells. Although computer vision technology has been employed for defect detection in the production processes, it is still difficult to achieve high accuracy in detecting defects in PV cells using traditional machine vision methods due to defect similarity and complex background. To address this challenge, a deep-learning-based quality assessment algorithm of MLC-Si wafers is proposed. Focusing on the dislocation defects, four different deep learning models are used to conduct migration learning and selected different optimizers (ADAM and SGDM) are used to optimize the network models, achieving good results in evaluating and comparing the quality of ML-Si wafers. On this basis, an improved network model MVGG-19 based on the VGG-19 is designed to improve the prediction accuracy further. The experimental results show that the prediction error of the improved network model is reduced by 63% (compared with VGG-19) and the reasoning speed reaches 10.22 FPS, indicating good detection performance.

Impact of Threading Dislocations Detected by KOH Etching on 4H-SiC 650 V MOSFET Device Failure after Reliability Test

In this study, the correlation between the Emission Microscopy (Em.Mi.) related to the failure site of the 4H-SiC 650V MOSFET devices after reliability test and epitaxial dislocation defects is presented. Devices failed at the High-Temperature Reverse Bias (HTRB) test were considered. Device layers have been stripped out by chemical wet etching and etched in a high temperature KOH solution to characterize defects emerging at the SiC surface. This approach was used to correlate failure emission spots with underlying structure of the material. KOH etching process on delayered devices was performed at 500°C for 10 minutes and then analysis by optical microscopy and SEM was carried out for defect classification and correlation with failure location.

Channelization cascade in landscape evolution

The hierarchy of channel networks in landscapes displays features that are characteristic of nonequilibrium complex systems. Here we show that a sequence of increasingly complex ridge and valley networks is produced by a system of partial differential equations coupling landscape evolution dynamics with a specific catchment area equation. By means of a linear stability analysis we identify the critical conditions triggering channel formation and the emergence of characteristic valley spacing. The ensuing channelization cascade, described by a dimensionless number accounting for diffusive soil creep, runoff erosion, and tectonic uplift, is reminiscent of the subsequent instabilities in fluid turbulence, while the structure of the simulated patterns is indicative of a tendency to evolve toward optimal configurations, with anomalies similar to dislocation defects observed in pattern-forming systems. The choice of specific geomorphic transport laws and boundary conditions strongly influences the channelization cascade, underlying the nonlocal and nonlinear character of its dynamics.

Pinning dislocations in colloidal crystals with active particles that seek stacking faults

By designing the shape of an active particle, its transport through a dense crystal can be tailored, as well as its interaction with dislocation defects present in the host crystal.

Generation of edge dislocation defects in Co3O4 catalysts: an efficient tactic to improve catalytic activity for oxygen evolution

A pioneering way is demonstrated to synthesize Co3O4 catalysts with abundant edge dislocation defects, which exhibit excellent OER catalytic performance.

Threading Dislocations in Metamorphic Semiconductor Buffer Layers Containing Chirped Superlattices

Metamorphic realization of semiconductor devices has become increasingly important due to the great freedom it affords in layer compositions and thicknesses. However, metamorphic growth is often accompanied by the introduction of high densities of threading dislocation defects. This behavior may be understood by using an annihilation and coalescence model for the threading dislocation behavior which is based on the dislocation interaction length Lint. For its application we considered only glide of dislocations, so the interaction length was assumed to be equal to the length of misfit dislocation segments LMD. The length of misfit segments was determined approximately by the Matthews, Mader, and Light model [J. Appl. Phys., 41, 3800 (1970)] for lattice relaxation, and was assumed to be independent of the distance from the interface. Within this framework we have applied the annihilation and coalescence model to chirped semiconductor superlattices to evaluate these superlattices as strainrelaxed buffers for metamorphic devices. In this work we have studied two basic types of InGaAs/GaAs chirped superlattice buffers: type I superlattices are compositionally modulated while type II superlattices are thickness modulated.