Optics Express · 2021 · 17 citations · 27 references
Electrical EngineeringMulti-mode InterferenceEngineeringQuantum ComputingMachine LearningQuantum Machine LearningComputer EngineeringLow Insertion LossComputer ScienceInverse DesignMmi Power SplitterInterference RegionDeep LearningMicroelectronicsSignal ProcessingOptoelectronics
The asynchronous double deep Q-learning (A-DDQN) method is proposed to design the multi-mode interference (MMI) power splitters for low insertion loss and wide bandwidth from 1200 to 1650 nm wavelength range. By using A-DDQN to guide hole etchings in the interference region of MMI, the target splitting ratio (SR) can be obtained with much less CPU time (about 10 hours for one design) and more effective utilization of the computational resources in asynchronous/parallel manner. Also, this method can simplify the design by using relatively few holes to obtain the same SR with small return loss.
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