Showing papers by "Robert A. Minasian published in 2022"

Athermal Microwave Photonic Sensor Based on Single Microring Resonance Assisted by Machine Learning

Abstract: We propose a machine learning (ML) assisted athermal microwave photonic (MWP) sensing scheme with high resolution based on a single microring resonance. The immunity of temperature interference of the high-resolution sensing is achieved by employing MWP sideband processing based interrogation, and supervised machine learning based on support vector regression (SVR) and neural tangent kernel (NTK) that are effective on small datasets. The MWP sideband processing transforms the variation of the target measurand into the shift of an ultra-deep notch in the radio frequency (RF) spectrum relieving the fabrication requirements on the microresonators, while ML accurately predicts the measurand by using the modulator bias voltage or RF passband transmission together with the RF notch position. The proposed sensor is demonstrated for relative humidity (RH) measurements using a silicon-on-insulator microring coated with polymethyl methacrylate. About 50 dB high RF rejection ratio is achieved over the sensing process, indicating a high sensing resolution. Despite the small experimental datasets, the established SVR and NTK models consistently exhibit lower mean absolute errors (MAEs) than the linear regression model in the RH prediction in the presence of temperature drifts. The NTK models achieve the lowest MAEs of 1.01% RH and 1.03% RH when the RF passband transmission and modulator bias are selected as the model input, respectively. The equivalent performances of the RF passband transmission and modulator bias voltage further demonstrate the feasibility of athermal sensing based solely on the MWP interrogation results of a single microring resonance, which simplifies the design and reduces the complexity.

Microwave Photonic Sensor Based on Optical Sideband Processing with Linear Frequency-modulated Pulse

Abstract: In this paper, a new microwave photonic (MWP) sensing scheme, which is based on interrogating microresonator sensors with fast speed and improved resolution by adopting a broadband linear frequency-modulated pulse (LFMP) in the MWP sideband processing, is presented. The LFMP modulates the interrogation light, creating the optical sideband that sweeps through the resonance rapidly. By using the optimized DC bias point, the resonance spectral dip with arbitrary characteristics can be transformed into the zero point in the temporal envelope of the transmitted LFMP, hence providing improved interrogation resolution of the resonance shifts caused by environmental changes. The proposed scheme was implemented with a microdisk resonance for temperature sensing, where up to 20-fold interrogation resolution improvement was demonstrated by tuning the DC bias voltage to the optimum. The interrogation speed is 500 kHz, which can be further improved by using a shorter repetition period and pulse width.

Inverse design of nano-photonic wavelength demultiplexer with a deep neural network approach

Abstract: 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 of 2.6×2.6µm2, a high transmission efficiency with a transmission loss of -2dB, a low reflection of -10dB, and low crosstalk around -7dB simultaneously.