Increasing demand for high density and broadband photonic integrated circuit (PIC) has motivated designs for photonic devices with high performance and compact footprint. However, the number of parameters in traditional photonic device designs is limited by the working principles for such devices, which often results in a perceived trade-off among device performances, such as bandwidth, efficiency, and footprint. Nonlinear optimizations such as direct binary search (DBS) and genetic algorithms (GA) have been explored in photonic designs, yet they have drawbacks such as slow convergence time in the range of 96-140 hours. By contrast, designs based on deep learning model relates device performances (output) and device parameters (inputs) via data-driven methodology, which enables arbitrarily large number of design parameter space that may overcome the perceived trade-off in traditional photonic designs. This work proposes a design methodology based on a combination of deep learning model and gradient descent method for photonic power splitter with arbitrary splitting ratio. Using pixel-based device geometry, a deep learning model relating the geometric parameters and device spectral performance is first established. Afterwards, a figure of merit based on a target splitting ratio is optimized through gradient descent method to yield the corresponding pixel-based device geometry. We demonstrate this method in the design of photonic power splitter with splitting ratio from 0.25 to 10, with insertion loss between 0.5dB to 1.16dB, and device size smaller than 16 μm2. In comparison with the genetic algorithm and direct binary search method, our proposed method is much faster in terms of convergence time.
We demonstrate a system-level low-power contactless button using MEMS ScAlN-based pyroelectric detector. As pyroelectric detectors can sense instantaneous temperature change, the human finger can act as a thermal source to activate the button. Using our in-house fabricated ScAlN-based pyroelectric detector which does not require any IR source, we package it into a contactless button system designed with electrical read-out circuits and signal processing. This contactless button system could detect the presence of a finger at a center distance measured up to ~4 cm away, ~2 cm radius circle area, suitable for application as contactless elevator button. Our contactless button system using ScAlN-based pyroelectric effect is characterized, tested and compared with a commercial contactless button. The power consumed is measured ~3.5× lower than that of commercial contactless button. The results obtained provide a potential solution towards energy efficient low-power contactless button system.
Gas sensors have wide applications including industrial process control, environment monitoring, safety control, etc. The distribution of these sensors enables data generation for the emerging trend of big data and internet of things. In this work, chip-based non-dispersive infrared (NDIR) gas sensors are demonstrated. Silicon substrate-integrated hollow waveguide (Si-iHWG), which is formed through silicon wafer etching and bonding, is used as optical channel and gas cell. A high sensitivity of 50 ppm for CO2 sensing is demonstrated. The Si-iHWG chip-based sensor with compactness, low cost, versatility, and robustness provides a promising platform for miniaturized gas sensing in various application scenarios.
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