Directly grown III-V quantum dot (QD) laser on on-axis Si (001) is a good candidate for achieving monolithically integrated Si photonics light source. Nowadays, laser structures containing high quality InAs / GaAs QD are generally grown by molecular beam epitaxy (MBE). However, the buffer layer between the on-axis Si (001) substrate and the laser structure are usually grown by metal-organic chemical vapor deposition (MOCVD). In this paper, we demonstrate all MBE grown high-quality InAs/GaAs QD lasers on on-axis Si (001) substrates without using patterning and intermediate layers of foreign material.

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The direct growth of a III–V compound semiconductor on Si(001) is an unsolved problem for monolithically integrated photonic devices on the Si platform. Here, we report the growth of a high-quality GaAs layer on on-axis Si(001) substrates by MBE. A single domain GaAs layer was grown on top of a AlGaAs nucleation layer on a Si(001) substrate. By optimizing the Al content of the nucleation layer, anti-phase domains were self-eliminated at the GaAs layer. This result represents a key step towards the realization of monolithically integration of III–V devices on the Si platform using direct epitaxial growth.

Reflection high-energy electron diffraction (RHEED) has wide application because it allows in situ observation of the sample surface behavior during molecular beam epitaxy growth. In particular, the RHEED pattern has been used as a milestone for growth condition calibration because it dynamically changes depending on the sample temperature, material supply rate, and supply ratio. However, RHEED pattern analysis depends on the accumulated know-how of the operator and has a time limitation; thus, its application to real-time feedback control is difficult. Moreover, with the conventional computerization method, it is difficult to correctly reflect and recognize the changes in RHEED due to changes in the observation conditions. On the other hand, the machine learning method using the convolutional neural network (CNN) recognizes feature points in the input database and is suitable for the classification of images with variability. In this study, we propose a measurement method for identifying the RHEED pattern of GaAs substrates during continuous rotation and build a data set of the growth conditions. A classification model is established by training the deep learning model using CNN, and is found to be more than 99% accurate. This is expected to be useful in the field of highquality III–V growth on GaAs.

Quantum dot infrared photodetectors (QDIPs) are receiving attention as next generation infrared photodetectors that offer high-sensitivity and high-temperature operation. The realisation of QDIPs on silicon (Si) substrates offer further great advantages in terms of cost reduction and higher-resolution focal plane arrays. Indium arsenide/gallium arsenide QDIPs grown directly on on-axis Si (100) substrates are demonstrated. These are expected to further reduce fabrication costs by utilising both the monolithic integration of QDIPs with Si readout integrated circuits and also the bare substrate cost (compared with offcut Si substrates). In the device, the peak detectivity at a temperature of 32 K is measured to be 5.8 × 10 7 cm Hz 1/2 /W of 6.2 μm at a bias 0.6 V, with a corresponding responsivity of 27 mA/W. This result indicates that QD structures directly grown on on-axis Si substrates are very promising for the realisation of high-performance QDIPs with low fabrication cost.