SlitNET: A Deep Learning Enabled Spectrometer Slit.

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The efficiency and resolution of dispersive spectrometers play crucial roles in optical spectroscopy. Achieving optimal analytical performance in optical spectroscopy requires striking a delicate balance between employing a narrow spectrometer input slit to enhance spectral resolution while sacrificing throughput or utilizing a wider slit to increase throughput at the expense of resolution. Here, we introduce a spectrometer slit empowered by a deep learning model SlitNET. We trained a neural network to reconstruct synthetic Raman spectra with enhanced resolution from low-resolution inputs. Subsequently, we performed transfer learning from synthetic data to experimental Raman data of materials. By fine-tuning the model with experimental data, we recovered high-resolution Raman spectra. This enhancement enabled us to distinguish between materials that were previously indistinguishable when using a wide slit. SlitNET achieved a resolution enhancement equivalent to employing a 10 μm slit size but with a physical input slit of 100 μm. This, in turn, enables us to simultaneously achieve high throughput and resolution, thereby enhancing the analytic sensitivity and specificity in optical spectroscopy. The incorporation of deep learning into spectrometers highlights the convergence of photonic instrumentation and artificial intelligence, offering improved measurement accuracy across various optical spectroscopy applications.