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We introduce and experimentally demonstrate a miniaturized integrated spectrometer operating over a broad bandwidth in the short-wavelength infrared (SWIR) spectrum that combines an add-drop ring resonator narrow band filter with a distributed Bragg reflector (DBR) based broadband filter realized in a silicon photonic platform. The contra-directional coupling DBR filter in this design consists of a pair of waveguide sidewall gratings that act as a broadband filter (i.e., 3.9 nm). The re-directed beam is then fed into the ring resonator which functions as a narrowband filter (i.e., 0.121 nm). In this scheme the free spectral range (FSR) limitation of the ring resonator is overcome by using the DBR as a filter to isolate a single ring resonance line. The overall design of the spectrometer is further simplified by simultaneously tuning both components through the thermo-optic effect. Moreover, several ring-grating spectrometer cells with different central wavelengths can be stacked in cascade in order to cover a broader spectrum bandwidth. This can be done by centering each unit cell on a different center wavelength such that the maximum range of one-unit cell corresponds to the minimum range of the next unit cell. This configuration enables high spectral resolution over a large spectral bandwidth and high extinction ratio (ER), making it suitable for a wide variety of applications. 

Surface-enhanced Raman spectroscopy (SERS) is an attractive method for bio-chemical sensing due to its potential for single molecule sensitivity and the prospect of DNA composition analysis. In this manuscript we leverage metal specific chemical enhancement effect to detect differences in SERS spectra of 200-base length single-stranded DNA (ssDNA) molecules adsorbed on gold or silver nanorod substrates, and then develop and train a linear regression as well as neural network models to predict the composition of ssDNA. Our results indicate that employing substrates of different metals that host a given adsorbed molecule leads to distinct SERS spectra, allowing to probe metal-molecule interactions under distinct chemical enhancement regimes. Leveraging this difference and combining spectra from different metals as an input for PCA (Principal Component Analysis) and NN (Neural Network) models, allows to significantly lower the detection errors compared to manual feature-choosing analysis as well as compared to the case where data from single metal is used. Furthermore, we show that NN model provides superior performance in the presence of complex noise and data dispersion factors that affect SERS signals collected from metal substrates fabricated on different days.