  Self-assembled InAs/GaAs quantum dots (QDs) have properties highly valuable
for developing various optoelectronic devices such as QD lasers and single
photon sources. The applications strongly rely on the density and quality of
these dots, which has motivated studies of the growth process control to
realize high-quality epi-wafers and devices. Establishing the process
parameters in molecular beam epitaxy (MBE) for a specific density of QDs is a
multidimensional optimization challenge, usually addressed through
time-consuming and iterative trial-and-error. Here, we report a real-time
feedback control method to realize the growth of QDs with arbitrary density,
which is fully automated and intelligent. We developed a machine learning (ML)
model named 3D ResNet 50 trained using reflection high-energy electron
diffraction (RHEED) videos as input instead of static images and providing
real-time feedback on surface morphologies for process control. As a result, we
demonstrated that ML from previous growth could predict the post-growth density
of QDs, by successfully tuning the QD densities in near-real time from 1.5E10
cm-2 down to 3.8E8 cm-2 or up to 1.4E11 cm-2. Compared to traditional methods,
our approach, with in situ tuning capabilities and excellent reliability, can
dramatically expedite the material optimization process and improve the
reproducibility of MBE, constituting significant progress for thin film growth
techniques. The concepts and methodologies proved feasible in this work are
promising to be applied to a variety of material growth processes, which will
revolutionize semiconductor manufacturing for optoelectronic and
microelectronic industries.
