This proposal addresses the urgent need for smart construction solutions in the Kingdom of Saudi Arabia (KSA), given the challenging desert terrain and extreme weather conditions. Autonomous haulage trucks, equipped with advanced sensors such as light detection and ranging (LiDAR), present a promising solution for efficient, large-scale material transport in these harsh environments. However, deploying this technology presents challenges, primarily because current LiDAR systems are not tailored for desert environments. Market-available LiDAR products, generally designed for urban traffic scenarios, fall short of the stringent requirements in autonomous mining applications. Traditional mechanical LiDAR systems are not suited to withstand the harsh temperatures and vibrations encountered in mines, and the presence of dense dust significantly reduces their ability to accurately detect and identify targets. To address these challenges, we will focus on improving the reliability of LiDAR hardware and the functionality of its software. The goal is to develop a solid-state LiDAR prototype that incorporates 3D point cloud processing capabilities, making it suitable for the mining operations in complex desert environments. Our project focuses on enhancing the solid-state Frequency Modulated Continuous Wave (FMCW) LiDAR systems, particularly on developing FMCW laser sources with narrow linewidths for long-range detection, strong anti-vibration, and high thermal stability. To achieve scalable and compact laser sources, quantum dot (QD) lasers and semiconductor optical amplifiers (SOAs) will be integrated onto silicon photonics via heterogeneous integration. This strategy significantly simplifies the assembly of active components like lasers, amplifiers, and photodetectors onto a heterogeneous III-V/Si platform, thereby reducing both complexity and cost. A detailed model for frequency-modulated light sources will be established, incorporating a closed-loop calibration algorithm and a coordinated control mechanism for an auxiliary on-chip optical path. To boost the environmental adaptability of this LiDAR prototype, perception and prediction algorithms will be embedded. A novel 3D point clouds denoising method will be introduced, utilizing both spatial and temporal properties to address interference from airborne dust in sandy environments. Vision Transformers (ViT) will be utilized for advanced feature extraction, enhancing the system's ability to understand complex 3D environments and interactive objects, thereby ensuring accurate instance segmentation. Given the coexistence of autonomous and manned vehicles in mining environments that creates complex traffic scenarios, a prediction algorithm will be developed based on inverse reinforcement learning, to forecast decisions and trajectories of vehicles identified by LiDAR. Altogether, this comprehensive strategy will enhance navigational safety and operational efficiency of autonomous haul trucks within the challenging desert landscapes of Saudi Arabia. The FMCW solid-state LiDAR prototype developed in this project will feature advanced hardware capabilities, including Output Average Optical Power ≥ 30 mW, Horizontal Field of View ≥ 120°, Vertical Field of View ≥ 30°, and Angular Resolution≤0.15°. On the software side, it will offer precise target segmentation functionality with overall accuracy≥70%, along with prediction capabilities that ensure Average Displacement Error ≤ 2m and Final Displacement Error ≤5m within the subsequent 8 seconds. The performance of the prototype will be rigorously tested and verified on the KAUST campus. As the project progresses, various technologies developed could be made available for licensing, offering significant commercialization prospects. This research will facilitate the wider implementation of autonomous haulage in smart construction sites across KSA, tackling the unique challenges posed by the desert terrain and extreme weather conditions.

BAS/1/1700-01-01

xiangpeng.ou@kaust.edu.sa

FMCW LiDAR ; QD lasers

Photonics, Physics, Semiconductors

The students are expected to acquire the basic knowledge of the design of the integrated silicon photonics chips, proficient skills of device designs using simulation tools, hand-on experimental experiences of optoelectronic device characterizations, and conference/journal publications.

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

Thin-film lithium niobate (TFLN), often termed the "silicon of photonics," is distinguished by its strong electro-optic response (r33 = 30 pm/V), wide transparency window (400 nm – 5 μm), and high refractive index (~2.2). TFLN wafers merge the benefits of traditional bulk LN devices with smaller footprints and lower power consumption, through scalable fabrication methods similar to those in silicon photonics. Available in diameters up to 6 inches—with 8 inches on the horizon—these wafers showcase propagation losses of < 0.03 dB/cm in waveguides and Q factors exceeding 108 in microresonators. In addition to exceptional modulator metrics (bandwidths >

BAS/1/1700-01-01

xiangpeng.ou@kaust.edu.sa

Quantum Dot Lasers; Thin-film lithium niobate

Photonics, Physics, Semiconductors

The students are expected to acquire the basic knowledge of the design of the integrated silicon photonics chips, proficient skills of device designs using simulation tools, hand-on experimental experiences of optoelectronic device characterizations, and conference/journal publications.

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

The rise of AI-driven services has triggered a significant increase in the demand for both processing capacity and energy efficiency. Currently, the performance of electronic processors is limited by power dissipation, integration density, and clock speed, especially for matrix-vector multiplication (MVM), which comprises more than 80% of the operations in modern AI models. In contrast, optical neural networks (ONNs) provide a promising alternative by exploiting intrinsic photon properties to calculate MVM, offering ultra-high bandwidth and processing frequency (>100GHz), ultra-low power consumption (sub-pJ/bit), and high parallelism through additional dimension division multiplexing. Silicon photonics, with its advantages of CMOS compatible manufacturability and high-density integration capacity, stands out as a promising platform for the realization of ONNs. However, the inherent indirect bandgap of silicon has hindered the progress of on-chip light sources compared to other photonic devices, creating a significant bottleneck for industrial-scale production. Leveraging Intel’s silicon photonics platform, our team earlier has led the integration of quantum dot (QD) lasers with silicon photonic integrated circuits (PICs). In this project, we aim to further develop a volume-manufacturable, fully integrated ONN system with high bandwidth and energy efficiency. This is in contrast to the bulky off-chip solution that requires ~(2dB+6dB) power in coupling and modulation alone. A key component of this system is the compact, energy-efficient, and robust QD laser source, which will be achieved via efficient coupling to planar waveguides, optical modulators, buffers, and wavelength multiplexers in the Si-on-insulator PIC platform. After addressing critical challenges in on-chip light sources, we will co-design hardware and software for a fully integrated ONN system. We propose a crossbar architecture utilizing micro-ring arrays, which maximizes on-chip laser benefits by offering compactness and high parallelism. Simulations of our preliminary ONN model have demonstrated over 91% recognition accuracy on the Modified National Institute of Standards (MNIST) dataset with an overall system computational density exceeding 0.27 TOPS/mm2. Leveraging a 65 nm standard CMOS process line at Intel and the on-chip non-volatile memory, we anticipate an experimental enhancement in computational density by over 70x compared to Google’s TPU accelerator, with a theoretical minimum power consumption reduction of over 1000x.

BAS/1/1700-01-01

xuhao.wu@kaust.edu.sa

optical neural network

Photonics, Physics, Semiconductors

The students are expected to acquire the basic knowledge of the design of the integrated silicon photonics chips, proficient skills of device designs using simulation tools, hand-on experimental experiences of optoelectronic device characterizations, and conference/journal publications.

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

Integrated silicon photonics has sparked a significant ramp-up of investment in both academia and industry as a scalable, power-efficient, and eco-friendly solution. At the heart of this platform is the light source, which in itself, has been the focus of research and development extensively. We tackle this from two perspectives: device-level and system-wide points of view. In the former, the different routes taken in integrating on-chip lasers are explored from different material systems to the chosen integration methodologies. In the latter, we seek system-wide applications that show great prospects in incorporating photonic integrated circuits (PIC) with on-chip lasers and active devices, namely, optical communications and interconnects, optical phased array-based LiDAR, sensors for chemical and biological analysis, integrated quantum technologies, and finally, optical computing. By leveraging the myriad inherent attractive features of integrated silicon photonics, we aim to inspire further development in incorporating PICs with on-chip lasers in, but not limited to, these applications for substantial performance gains, green solutions, and mass-production. In this VSRP program, students will have the opportunity to learn the latest device design and fabrication of the integrated silicon photonics chips, and explore the applications based on their own interests. The project needs 3-6 months to be completed. Successful students can likely publish their results in prestigious scientific venues and get enrolled in the Ph.D program in Integrated Photonics Lab.

BAS/1/1700-01-01

alkhazraji_e@jic.edu.sa

integrated Si photonics

photonics Engineering, Physics, Mathematics

The students are expected to acquire the basic knowledge of the design of the integrated silicon photonics chips, proficient skills of device designs using simulation tools, hand-on experimental experiences of optoelectronic device characterizations, and conference/journal publications.

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

Graduate or Undergraduate