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基于微纳集成的光波导相控阵芯片是近年来激光雷达技术领域的研究热点. 随着激光雷达系统空间分辨这一实际应用需求的不断提高, 作为激光雷达系统中的光束控制器件, 光波导相控阵需要扩大阵列规模以提升输出光束的空间分辨率. 同时也为光波导相控阵输出光束的优化校准带来了困难, 现有算法不仅光束校准质量不高, 且校准效率较低. 为此, 本文将Adam算法应用于光波导相控阵输出光束校准系统中, 通过建模仿真比较了Adam算法与现有SPGD算法和GS算法在光束校准层面上的优劣. 同时, 搭建实验系统实现了高质量的光束校准, 根据校准结果, 在Adam算法校准下光波导相控阵输出光束的主旁瓣比优于15.98 dB, 对16×16光波导相控阵输出光束校准达到收敛所需的迭代次数低于600次. 这一算法在光波导相控阵输出光束校准方面的应用, 能够提高光波导相控阵的控制精度和效率, 拓展光波导相控阵在激光雷达技术、数字全息技术和生物成像技术等方面的应用.
Optical phased array (OPA) technology, as a pivotal component of laser detection and ranging (LiDAR) systems, plays a crucial role in augmenting the application efficiency in fields such as autonomous driving, precision measurement, and remote sensing detection. With the escalating demands for high-resolution imaging, the array size of OPAs is continuously expanding, imposing higher requirements on the calibration precision and efficiency of the output beam. Existing calibration algorithms, such as the simultaneous perturbation stochastic gradient descent (SPGD) and the Gerchberg-Saxton (GS) algorithm, often face challenges of prolonging calibration times and insufficient precision when dealing with large-scale OPA systems. In order to address this problem, our study introduces the Adam optimization algorithm, renowned for its adaptive learning rate feature, into the calibration process of OPA output beams. Through simulation modeling and experimental validation, this work comprehensively examines the differences in performance between the Adam algorithm and conventional SPGD and GS algorithms in beam calibration, especially under various OPA array configurations. For a 16×16 OPA array, the application of the Adam algorithm significantly enhances the peak side lobe ratio (PSLR) to over 15.98dB, while notably reducing the number of iterations to less than 600, thereby shortening the calibration cycle and improving calibration precision effectively. Furthermore, this work provides an in-depth analysis of parameter selection, convergence speed, and stability of the Adam algorithm in OPA calibration, offering detailed guidance for achieving more efficient and high-quality beam calibration. Through comparative analysis, this work not only demonstrates the substantial advantages of the Adam algorithm in enhancing OPA calibration efficiency, reducing calibration duration, and optimizing output beam quality but also emphasizes its critical role in advancing OPA technology. The main contribution of this work lies in providing an innovative algorithmic approach for achieving efficient calibration of OPA output beams, which has important theoretical and practical significance for advancing the LiDAR technology, particularly in the field of high-precision beam control. Moreover, by applying optimized algorithms, this study not only improves the performance of OPA technology within existing domains but also paves new ways for its application in emerging fields such as optical communication, optical networking, and high-resolution imaging. 
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Keywords:
- optical phased array /
- calibration algorithm /
- Adam algorithm
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图 2 OPA输出光束校准的Adam算法流程图
Fig. 2. Flowchart of the Adam algorithm for calibrating the OPA output beam.
图 3 OPA输出光束校准的SPGD算法流程图
Fig. 3. Flowchart of the SPGD algorithm for calibrating the OPA output beam.
图 4 使用Adam算法对不同阵列规模OPA输出光束校准结果 (a) 4×4阵列校准结果; (b) 8×8阵列校准结果; (c) 16×16阵列校准结果
Fig. 4. Different adjusting results of output beam in serial OPA with Adam algorithm: (a) Adjusting results of 4×4 array; (b) adjusting results of 8×8 array; (c) adjusting results of 16×16 array.
图 5 使用SPGD, GS, Adam算法对4×4规模OPA输出光束校准结果 (a)不同算法优化仿真结果; (b) 优化迭代1000次不同算法评价函数曲线图汇总; (c) 优化不限次数不同算法评价函数曲线图汇总
Fig. 5. Different adjusting results of output beam with SPGD, GS, Adam algorithm in 4×4 OPA: (a) Simulation results with different algorithms; (b) collection of curve graphs of evaluation function when iterating 1000 times with different algorithms; (c) collection of curve graphs of evaluation function when iterating unlimited times with different algorithms.
图 7 仿真理论分布、Adam算法及SPGD算法输出光场三维图
Fig. 7. 3 D diagram of output light field with simulation model, Adam algorithm and SPGD algorithm.
图 8 SPGD算法及Adam算法光束校准效果图 (a)不同迭代次数下Adam算法光场灰度图; (b) 不同迭代次数下SPGD算法光场灰度图; (c) Adam及SPGD算法评价函数曲线图
Fig. 8. Adjusting results of beam with SPGD and Adam algorithm: (a) Grey-scale map of different iterative times with Adam algorithm; (b) grey-scale map of different iterative times with SPGD algorithm; (c) curve graph of evaluation function with Adam and SPGD algorithm.
图 9 (a)优化光场叠加图; (b)不同位置优化光场
Fig. 9. (a) Superposed figure of optimized light field; (b) optimized light field in different positions.
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