Enhanced performance of on-chip integrated biosensor using deep learning

A new approach for determining the concentration composition of a multi-element media using a micro-ring resonator (MRR) is proposed which allows for both electrical and thermal noise removal as well as moderately higher average accuracy. This method uses two neural networks, namely a convolutional neural network (CNN) and a deep neural network (DNN). The CNN differentiates the transmission spectrum from the noise. This spectrum is used to obtain selected features before being fed into the DNN, which determines the concentration of each chemical in the analyte. Both models are trained to work using simulated data from a silicon on-insulator ring resonator operating between the infrared wavelengths of λ=1.46μm$$\lambda =1.46\,\upmu \hbox{m}$$ to λ=1.6μm$$\lambda =1.6\,\upmu \hbox{m}$$ on mixtures of water, ethanol, methanol, and propanol, although the same approach can be used with other designs and substances. The CNN was trained using the MRR transmission spectra superimposed with white Gaussian noise as well as Poisson noise to mimic different noise sources, while the DNN underwent training on the extracted features. Average root-mean-square error for element concentration for the entire system is 0.0775% for a range of concentrations from 0.0357 to 75%, and the largest error had a value of 0.68% concentration.