The low-temperature growth of materials
that support
high-performance
devices is crucial for advanced semiconductor technologies such as
integrated circuits built using monolithic three-dimensional (3D)
integration and flexible electronics. However, low growth temperature
prohibits sufficient atomic diffusion and directly leads to poor material
quality, imposing severe challenges that limit device performance.
Here, we demonstrate superior quality growth of 3D semiconductors
at growth temperatures reduced by >200 °C by using two-dimensional
(2D) materials as intermediate layers to optimize the potential energy
barrier for adatom diffusion. We reveal the benefits of maintaining,
but reducing, the potential field through the 2D layer, which coupled
with the inert surface of the 2D material lowers the kinetic barriers,
enabling long-distance atomic diffusion and enhanced material quality
at lower growth temperatures. As model systems, GaN and ZnSe, grown
using WSe2 and graphene intermediate layers, exhibit larger
grains, preferred orientation, reduced strain, and improved carrier
mobility, all at temperatures lower by >200 °C compared to
direct
growth as characterized by diffraction, X-ray photoelectron spectroscopy,
Raman, and Hall measurements. The realization of high-performance
materials using 2D intermediate layers can enable transformative technologies
under thermal budget restrictions, and the 2D/3D heterostructures
could enable promising heterostructures for future device designs.
This note studies the flow shop scheduling problems with the effects of exponential learning and simple linear deterioration. The objective functions are to minimize makespan, total completion, the sum of the th power of completion times and total lateness. We show that several flow shop problems can be solved in polynomial time.
We discuss single machine scheduling problems with learning effects of setup and removal times and deterioration effects of processing time, i.e., the processing (setup or removal) time of a job is a function of its position . The objective functions are finding the optimal sequence of jobs to minimize a cost function containing makespan, total completion time and total absolute differences in completion times and to minimize a cost function containing makespan, total waiting time and total absolute differences in waiting times. The problems are modeled as an assignment problem respectively, and thus can be solved in polynomial time.
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