论文标题
具有机器学习的光学波长仪表提高了精度
Optical Wavelength Meter with Machine Learning Enhanced Precision
论文作者
论文摘要
光子学和微波工程中的不同应用需要一种测量信号的瞬时频率的方法。光子实现通常应用配备三个或多个输出端口的干涉仪,以测量由光学延迟线提供的频率相关相移。构成干涉仪的组件容易导致损害,从而导致错误的测量。结果表明,要检索的信息是由位于三维笛卡尔对象空间内圆锥体上的三部分矢量编码的。测得的数据属于描述干涉仪的作用的线性图下对象空间的图像。在学习算法的辅助下,构建了从图像空间到对象空间的逆图。逆地图可以补偿各种障碍,同时又对噪声强大。仿真结果表明,在干涉仪模型捕获所有重大障碍的程度上,仅由随机噪声水平限制了精度。在SI3N4光子集成平台上制造了波长仪体结构,以实验证明该方法。与常规方法相比,应用于测量的数据,通过所提出的方法可以实现精度的数量级改善。
Diverse applications in photonics and microwave engineering require a means of measurement of the instantaneous frequency of a signal. A photonic implementation typically applies an interferometer equipped with three or more output ports to measure the frequency dependent phase shift provided by an optical delay line. The components constituting the interferometer are prone to impairments which results in erroneous measurements. It is shown that the information to be retrieved is encoded by a three-component vector that lies on a circular cone within a three-dimensional Cartesian object space. The measured data belongs to the image of the object space under a linear map that describes the action of the interferometer. Assisted by a learning algorithm, an inverse map from the image space into the object space is constructed. The inverse map compensates for a variety of impairments while being robust to noise. Simulation results demonstrate that, to the extent the interferometer model captures all significant impairments, a precision limited only by the level of random noise is attainable. A wavelength meter architecture is fabricated on Si3N4 photonic integration platform to prove the method experimentally. Applied to the measured data, greater than an order of magnitude improvement in precision is achieved by the proposed method compared to the conventional method.