Abstract
Temperature control is a critical aspect for the semiconductor material growth of the Molecular Beam Epitaxy system. The growth rate depends on the temperature of the beam source, and the quality of epi-layers heavily depends on the temperature of the substrate. In this paper, we reported an intelligent temperature control algorithm based on the BP neural network, which is specially optimized for the Molecular Beam Epitaxy system. After training the BP neural network by the collected temperature data of the Molecular Beam Epitaxy, the intelligent temperature control algorithm keeps the temperature error within 0.1°C. Moreover, this intelligent algorithm in the field of Molecular Beam Epitaxy achieves a better control effect, reduces the regulation time of the system and makes the system overshoot to zero.
Highlights
The Molecular beam epitaxy (MBE) is an ultrahigh vacuum technique for growing skinny epitaxial layers of semiconductor crystals
The MBE source is usually provided by Knudsen effusion cells, where the flux of the molecular beam is adjusted by the cell operation temperature, resulting in an appropriate range of growth rate
The PID control algorithm with BP neural network is based on the prediction of the overall operation status of the object to determine the future operation status so as to determine the control quantity
Summary
The Molecular beam epitaxy (MBE) is an ultrahigh vacuum technique for growing skinny epitaxial layers of semiconductor crystals. Liang et al [16] designed a heater autotuned, which performed high-precision temperature control in a variety of constantly changing weather conditions without advance knowledge of the weather variation range and showed BP-PID could achieve a high-precision control effect under complex conditions. All, these previous studies show that the PID temperature control based on BPNN is an effective method to resolve nonlinear parameter issues. The comparison of traditional PID control and BP-PID control in the MBE system was studied
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