  In this paper, we propose a pre-trained-combined neural network (PTCN) as a
comprehensive solution to the inverse design of an integrated photonic circuit.
By utilizing both the initially pre-trained inverse and forward model with a
joint training process, our PTCN model shows remarkable tolerance to the
quantity and quality of the training data. As a proof of concept demonstration,
the inverse design of a wavelength demultiplexer is used to verify the
effectiveness of the PTCN model. The correlation coefficient of the prediction
by the presented PTCN model remains greater than 0.974 even when the size of
training data is decreased to 17%. The experimental results show a good
agreement with predictions, and demonstrate a wavelength demultiplexer with an
ultra-compact footprint, a high transmission efficiency with a transmission
loss of -2dB, a low reflection of -10dB, and low crosstalk around -7dB
simultaneously.
