Broadband semiconductor light sources operating at 1060 nm based on InAs:Sb/GaAs submonolayer quantum dots

In this paper, we investigate the potential of submonolayer-grown InAs:Sb/GaAs quantum dots as active medium for opto-electronic devices emitting in the 1060 nm spectral range. Grown as multiple sheets of InAs in a GaAs matrix, submonolayer quantum dots yield light-emitting devices with large material gain and fast recovery dynamics. Alloying these structures with antimony enhances the carrier localization and red shifts the emission, whereas dramatically broadening the optical bandwidth. In a combined experimental and numerical study, we trace this effect to an Sb-induced bimodal distribution of localized and delocalized exciton states. While the former do not participate in the lasing process, they give rise to a bandwidth broadening at superluminescence operation and optical amplification. Above threshold laser properties like gain and slope efficiency are mainly determined by the delocalized fraction of carriers

Simplified subspaced regression network for identification of defect patterns in semiconductor wafer maps

Wafer defects, which are primarily defective chips on a wafer, are of the key challenges facing the semiconductor manufacturing companies, as they could increase the yield losses to hundreds of millions of dollars. Fortunately, these wafer defects leave unique patterns due to their spatial dependence across wafer maps. It is thus possible to identify and predict them in order to find the point of failure in the manufacturing process accurately. This paper introduces a novel simplified subspaced regression framework for the accurate and efficient identification of defect patterns in semiconductor wafer maps. It can achieve a test error comparable to or better than the state-of-the-art machine-learning (ML)-based methods, while maintaining a low computational cost when dealing with large-scale wafer data. The effectiveness and utility of the proposed approach has been demonstrated by our experiments on real wafer defect datasets, achieving detection accuracy of 99.884% and R2 of 99.905%, which are far better than those of any existing methods reported in the literature