硅基波导集成光学相控阵芯片——设计难点与突破(特邀)

Silicon-Based Waveguide Integrated Optical Phased Array Chips for LiDAR:Design Challenges and Breakthroughs(Invited)

以自动驾驶为代表的移动应用正在大力推动激光雷达向标准化和轻量化发展,新一代的固态激光雷达有望在芯片上单片集成,类似于电子芯片自由部署在智能传感平台上,大幅降低量产成本和应用门槛。在硅基平面波导集成平台上,利用半导体工艺并以光学相控阵为集成范式实现芯片化的固态激光雷达,不仅能够实现数字化的激光波束调控和软件定义的自适应扫描,而且能够最大限度地减少分立光学组件的高精度装调,提高系统在复杂工况下的可靠性。从光学相控阵的波束成形和波束扫描出发,对标自动驾驶激光雷达的典型应用需求,分析波导集成光学相控阵设计的理论参数和设计挑战,综述当前学界在大规模集成上的进展和突破,着重介绍激光扫描赋能、异质异构集成和指向混淆消除方面的工作,并对后续发展进行展望。

Significance Light detection and ranging(LiDAR)measures the distances to detectable targets using time-of-flight depth sensing.When coupled with laser beam scanners or arranged in paired emitter-receiver arrays on focal planes,LiDAR systems capture surroundings to create precise 3-D point-cloud representations of the scene.A static point cloud can create a digital twin of physical objects for preservation,inspection,or modeling purposes,which are widely used in fields like surveying,mapping,archaeology,and biological detection.Meanwhile,real-time point clouds,integrated with data from other sensors,dynamically identify and track various targets,which,in turn,facilitates interactive navigation in complex or dynamic environments.A key reason for the proliferation of LiDAR is its capability to offer camera-like resolution due to its near-infrared operating wavelength.LiDAR boasts three to five orders of magnitude of spatial resolution improvement compared to radar.Additionally,driven by the boom in autonomous driving,unmanned autonomous vehicles,and AI-enabled smart robotics,the LiDAR community has been endeavoring to reduce sensor footprint,power consumption,and manufacturing complexity.This effort subsequently lowers adoption costs and integration difficulties with the host platform.Nevertheless,current development primarily relies on miniaturizing discrete components such as lasers,photodetectors,optics,and beam scanners.As an active sensor that integrates power-hungry lasers and trans-impedance amplifiers,ensuring power integrity and managing thermal issues becomes challenging during denser integration.Moreover,it is crucial to achieve high-precision active optical alignment for assembling these discrete components.However,this process often leads to limited throughput,increased costs,and potential robustness issues in real-world applications.Lastly,as LiDAR performance improves in terms of resolution and frame rate,processing the dense point-cloud data becomes computationally intensive,which can bring about data fusion conflicts with other sensors.To address these issues,researchers have been exploring the potential of reinventing LiDAR using photonic integration platforms.Taking advantage of the maturing integration of planar light-wave circuits(PLCs)through semiconductor processes,chip-scale solid-state LiDAR offers diffraction-limited integration density,on-chip light manipulation with nano-watts power consumption at gigahertz refresh rates,digitally controlled addressability within the given field-of-view,close or even monolithic integration with electronic circuits,and potentially low cost with high throughput.Among these emerging PLC solutions,LiDAR chips in the form of optical phased arrays(OPAs)directly manipulate the synthesized laser phase front,providing features such as high-speed and seamless solid-state beam scanning and the ability to rapidly change beam direction within a large field of view.This capability enables adaptive scanning based on input cues from other sensors or recognition results from previous frames,allowing denser point clouds to be assigned to high-priority targets for improved real-time accuracy.In essence,OPA-based LiDARs can lock onto high-value targets and track multiple targets similar to their radio counterpart,the active electronically scanned array(AESA),thereby reducing the burden on communication bandwidth and processing power.Furthermore,optical phased arrays eliminate the need for discrete optical components,which not only facilitates manufacturing but also improves the robustness of the sensor.Therefore,an OPA-based LiDAR emits and receives light directly from a flat optical aperture without bulky lenses or mirrors,which helps to reduce the form factor and expand the available field of view.Over the last 15 years,a large number of OPAs have been developed and demonstrated,driven by a strong focus on realizing OPA-based solid-state LiDAR.In this paper,we aim to outline the key challenges and breakthroughs encountered during this development.Principle and Progress A notable distinction between an OPA and an AESA lies in the integration density constraint of dielectric waveguides for OPAs,making it challenging to achieve the half-wavelength condition typical in AESAs.Evaluating the beamforming principle as depicted in Eq.(1)allows for the analysis of periodicity and the formation of grating lobes illustrated in Fig.1.Advanced beamforming techniques,such as spatial filtering using the element factor,non-periodic array element arrangements,and applying amplitude taper across the array,are explained from a digital signal processing perspective and visually presented in Fig.2.Subsequently,beam steering principles and behaviors are derived and simulated in Eq.(3)and Fig.3,respectively.After introducing the beamforming and beamsteering characteristics of OPAs,we establish a relationship between design parameters and device performance.We then calculate the necessary design parameters against typical automotive LiDAR specifications outlined in Table 1,highlighting challenges like high-density integration of photonic components,exemplified in Fig.4.The requirement for a vast number of components further complicates the system,leading to losses and a field-of-view limited by aliasing.Breakthroughs in complexity reduction have been achieved through wavelength-tuning-assisted beam-steering,as illustrated in Fig.5,with recent advancements in the architecture of two-dimensional dispersive arrays detailed in Table 2.Specifically,we emphasize the need for narrow-linewidth lasers capable of wide-range wavelength tuning and introduce our external cavity laser,depicted in Fig.6.To mitigate insertion loss and boost beamforming efficiency,on-chip gain can be implemented via heterogeneous integration of III/V amplifiers on the silicon platform.The wafer-bonding process flow,pioneered by the University of California,Santa Barbara(UCSB)and Interuniversity Microelectronics Center(IMEC),is illustrated in Fig.7.Additionally,recent demonstrations of heterogeneous-integrated OPA LiDAR by Samsung are introduced and discussed.Another approach to improve the power budget involves increasing the power throughput of the device by combining high-power-handling silicon nitride waveguides with silicon waveguides that offer good mode confinement and efficient light modulation.An example of a multi-layered-integrated OPA is shown in Fig.8,with similar works detailed in Table 3.To extend the field of view beyond the aliasing-limited scanning range,methodologies for aperiodic array design are introduced,with achieved beam quality summarized in Table 4.In addition to aperiodic arrays,uniform arrays of vernier difference can be paired into a bi-static LiDAR system.Leveraging the mismatch between transmitting and receiving OPAs,this approach achieves high side-mode suppression over a broad field of view.Recent progress in such bi-static vernier arrays is listed in Table 5.Conclusions and Prospects In summary,all current performance achievements for OPA-based solutions are compiled in Table 6 and compared against nominal LiDAR specifications.It is evident that,except for the maximum detectable range and point rate,most LiDAR specifications have been met.This underscores the technology’s high readiness,especially with ongoing efforts to address longer-range operations through reduced insertion loss,on-chip amplification,and optimized throughput.Achieving a higher point rate will require a parallel multi-beam operation to surpass the measurement rate limited by photon round-trip travel time.Furthermore,there is a need for further investigation into challenges like chip yield,system-level integration during prototyping,as well as calibration and control during and after manufacturing.Simultaneously,adopting and leveraging advanced features such as adaptive scanning necessitates collaboration among LiDAR users and other stakeholders in the ecosystem.