光達(LiDAR)在現今已是非常融入生活的一項技術，因光達能夠快速且簡單的量測出發射端與反射物間的距離，可以降大幅降低程式運算成本，非常符合無人化機械在定義周圍物體距離或空間概念的需求。光達的種類大略能分成三種：機械式、固態式、混合式，三種狀況各有優劣，可以針對不同狀況使用不同的光達，此篇論文討論的為固態式光達的其中一種分支：晶片光達，此種光達結合了兩種非常成熟的技術，分別為以雷達相控陣列為基礎的光學相控陣列(OPA)以及能夠將光達系統的發射與接收整合在CMOS製程之矽光子積體化晶片上。
依照相位陣列可以實現LiDAR的光束的偏轉，此理論可以推出每根天線所需要的相位差，但在實際的晶片上，會有很多因素影響天線相位差，使光束成像不佳。因此使用基因演算法能夠將多個數值產生的結果，逐步往理論成像角度做收斂，以達到優化光學相控陣列的效果。我們先利用相位陣列理論，將遠場電場的公式分為element factor以及array factor，利用商用模擬軟體RSoft 模擬單根天線的遠場繞射圖作為element factor，再利用天線間距等參數完成array factor的出光角度計算，最後將element factor與array factor合併為理想遠場干涉電場，並用此電場作為基因演算法的適應函數，最後讓基因演算法在實驗晶片上，藉由繁殖，剔除較差適應度的電壓群，以進行電壓群之選擇，使實驗上的遠場干涉成像最接近理想值。
此篇論文主要會討論到關於在晶片LiDAR光束轉向的實驗上，如何利用基因演算法法迴圈考慮實驗迴圈，而基因演算法最重要的適應函數是如何用相位陣列理論計算出遠場電場干涉圖，且如何使用此遠場電場干涉圖與實驗掃描光強度對角度圖得到的值做比較並且化為同樣的論值做適應度分析。實驗上我們先使用基因演算法搜尋晶片LiDAR的0°光束，並且定義光強度最大值在此角發生，接著再用基因演算法搜尋其他角度的光束偏轉，例如30°、50°，達到優化相控陣列的效果，此間距1 μm之一維天線，可視場角約為100°，最後由基因演算法求出光束FWHM約為1.60，且SMSR約為5 dB。

LiDAR is already a technology that is closely integrated into life today. Because LiDAR can quickly and easily measure the distance between the transmitter and the reflector, it can significantly reduce the cost of programming and computing, which agrees with the needs of unmanned machinery in defining the distance or space concept of surrounding objects. LiDAR can be divided into three types: mechanical, solid-state, and hybrid. Each of these three conditions has its advantages and disadvantages. Different LiDAR can be used for different situations. The chip-based solid-state LiDAR, including two very mature technologies - optical phased array (OPA)-based radar phased array and silicon photonics-based integrated chip for the transmission and reception of LiDAR system through CMOS process, will be further discussed in this thesis.
According to the phased array, the deflection of LiDAR's beam can be achieved. This theory can introduce the phase difference required for each antenna. Still, many factors will affect the antenna phase difference in the actual wafer, making the poor beam imaging. Therefore, a genetic algorithm can gradually converge the results generated by multiple values to the theoretical imaging angle by optimizing the phased optical array. We first use the phased array theory to divide the formula of the far-field electric field into element factor and array factor. We use the commercial simulation software RSoft to simulate the far-field diffraction pattern of a single antenna as an element factor and then use the antenna spacing and other parameters to complete the calculation of the light exit angle of the array factor. Finally, the element and array factors are combined into an ideal far-field interference electric field, which is used as a fitness function of the genetic algorithm. Eventually, the genetic algorithm is adopted on the experimental chip to demonstrate the practical far-field interference imaging, closest to the ideal value, by breeding to remove the voltage group with poor fitness for the voltage group selection.
This thesis will mainly discuss using the genetic algorithm to consider the experimental loop in the chip-based LiDAR beam steering experiment. The most crucial fitness function of the genetic algorithm is how to use the phase array theory to calculate the far-field electric field interferogram, which will be compared with the values obtained by the angle diagram with the experimental scanning light intensity for fitness analysis. Experimentally, we first use a genetic algorithm to search for the 0° beam of the chip-based LiDAR and define the maximum light intensity at this angle. Then we use a genetic algorithm to search for beam deflection at other angles, such as 30°, 50°, to achieve the effect of optimizing the phased array. With this spacing of 1 µm in a one-dimensional antenna, the visual field of view is about 100°. Finally, the genetic algorithm finds that the beam FWHM is about 1.60, and the SMSR is about 5 dB.

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