The use of composite materials has seen widespread adoption in modern aerospace industry. This has been facilitated due to their favourable mechanical characteristics, namely, low weight and high stiffness and strength. For broader implementation of those materials though, the out-of-autoclave production processes have to be optimized, to allow for higher reliability of the parts produced as well as cost reduction and improved production speed. This optimization can be achieved by monitoring and controlling resin filling and curing cycles. Photonic Integrated Circuits (PICs), and, in particular, Silicon Photonics, owing to their fast response, small size, ability to operate at higher temperatures, immunity to electromagnetic interference, and compatibility with CMOS fabrication techniques, can offer sensing solutions fulfilling the requirements for composite material production using carbon fibres. In this paper, we demonstrate a passive optical temperature sensor, based on a 220 nm height Silicon-on-Insulator platform, embedded in a composite tool used for producing RTM-6 composite parts of high quality (for use in the aerospace industry). The design methodology of the photonic circuit as well as the experimental results and comparison with the industry standard thermocouples during a thermal cycling of the tool are presented. The optical sensor exhibits high sensitivity (85 pm/°C), high linearity (R² = 0.944), and is compatible with the RTM-6 production process, operating up to 180 °C.

We propose two configurations for the coexistence of sensing and communication signals, exploiting Free Space Optical (FSO) links to transmit commercial 6G traffic and remotely interrogate sensors distributed along distances of 100 m. The sensors are fabricated on a Silicon Photonics (SiPh) platform, to achieve miniaturization and highly compact form of the required features. The first scheme consists of couplers and circulators, while the second utilizes polarization handling elements to multiplex both signals, while a 100-m outdoor Free Space Optical link is utilized for both schemes. The configurations exhibit high stability, with uninterrupted transmission of traffic along the duration of the experiments, while the sensors’ performance is evaluated, with their discrepancies measured below 2%.

Composite materials have increased in use across multiple industries due to their low weight, thermal stability and design flexibility. As applications grow, the importance of accurate process-monitoring techniques grows, with various research teams investigating a variety of in situ cure-monitoring sensors. Photonic integrated circuits (PICs) can be a feasible solution in tracking curing cycles due to their resistance to harsh conditions and ease of integration into the production tool. This paper demonstrates the integration and performance evaluation of an embedded phase-shifted Bragg grating solution, instead of commonly used fiber sensors, based on 220 nm height silicon-on-insulator platform for in-situ monitoring of the RTM-6 resin curing process in a composite tool. The embedded optical sensor presents high sensitivity (75 pm/°C) and linearity (R²=0.995) up to 120 °C, enabling precise temperature measurements throughout the curing cycle. The experimental results show consistency with previously reported simulations, throughout the calibration, dry run characterization and the curing process, up to 120 °C, allowing for accurate monitoring of the curing process.

The cure stage of thermosetting composites production is critical for the overall process duration and manufacturing costs. While process simulations are commonly used to estimate cure behaviour, real-time predictive capabilities in resin transfer moulding (RTM) remain limited, primarily due to the computational cost of finite element (FE) methods. To address this gap and accurately estimate cure process parameters in RTM, this study proposes a surrogate cure simulation approach based on two state-of-the-art machine learning (ML) voting ensemble models – XGBoost and Light Gradient Boosting Machine – designed to predict cure time and temperature overshoot. To train the model, the cure of an epoxy/carbon fibre flat plate was simulated using the FE solver Marc, providing data over a wide range of conditions. The predictions of temperature overshoot and cure time demonstrate remarkable consistency and high accuracy (R ² values up to 98%) with execution times under 30 ms for both variables. Performance was validated against unseen simulation data and further verified through RTM manufacturing trials and differential scanning calorimetry (DSC), confirming cure completion and a final glass transition temperature of 191°C–194°C. Unlike existing studies that remain simulation-focused, this approach bridges process simulation and data-driven modelling, offering a practical tool for real-time optimisation in industrial RTM applications.

Light detection and ranging (Lidar) is a key enabling technology for autonomous vehicles and drones. Its emerging implementations are based on photonic integrated circuits (PICs) and optical phased arrays (OPAs). In this work, we introduce a novel approach to the design of OPA Lidar antennas based on Si₃N₄ grating couplers. The well-established TriPleX platform and the asymmetric double stripe waveguide geometry with full etching are employed, ensuring low complexity and simple fabrication combined with the low-loss advantages of the platform. The design study aims to optimize the performance of the grating coupler-based radiators as well as the OPA, thus enhancing the overall capabilities of Si₃N₄-based Lidar. Uniform and non-uniform grating structures are considered, achieving θ and φ angle divergences of 0.9° and 32° and 0.54° and 25.41°, respectively. Also, wavelength sensitivity of 7°/100 nm is achieved. Lastly, the fundamental OPA parameters are investigated, and 35 dBi of peak directivity is achieved for an eight-element OPA.

Carbon fiber-reinforced composites have become a staple in the aerospace industry, owing to their mechanical properties (low weight and mechanical stability). Resin transfer molding (RTM) is a manufacturing process for composite materials, offering significant cost reduction, compared to autoclave processes. To achieve the desired quality though, strict control of several process conditions is required. Monitoring the temperature and pressure conditions inside the tool along with the resin’s refractive index (RI) (monitoring its cure state), can provide tight control over the curing cycle. Leveraging silicon photonics advantages (electrically passive and stable at high temperatures) can enable accurate tracking of the resin curing conditions. In this work, the first demonstration of silicon photonic sensors in the production of composite parts is presented. The sensors’ high thermo-optic sensitivity (78.4 pm/°C) and linearity (0.9787) are leveraged, while being deployed inside a multiton composite tool. The devices are demonstrated under demanding temperature (up to $165~^{\circ }$ C) and pressure (7 bar) conditions, during a 6-h production cycle, compatible with an RTM-6 (aerospace-grade RTM) process. It shows highly stable performance across the full dynamic range ( $30~^{\circ }$ C– $165~^{\circ }$ C), accurately monitoring the cure state of the resin.