Machine Vision System Utilizing Black Silicon CMOS Camera for Through-Silicon Alignment

Abstract

Current development trends concerning miniaturizing of electronics and photonics systems are aiming at assembly and 3D co-integration of a broad range of technologies including MEMS, microfluidics, wafer level optics, and silicon photonics. To this end, on-chip integration using silicon-photonics platform offers a wide range of possibilities addressing passive optics functionality, active optoelectronic devices, and compatibility with CMOS fabrication. On the other hand, the hybrid technology enabling volume manufacturing of such systemon-chip components it is still in an early development stage. Here, a new type of machine vision system enabling precision stacking and bonding processes of III-V components on silicon photonics chips is introduced. In particular, we focus on the ability to see through substrates with high resolution, which is crucial for the alignment of the markers used for assembly of integrated components on silicon wafer. The system is based on the use of a black silicon enhanced CMOS sensor with extended wavelength response beyond transparency cut off wavelength for silicon substrate. We study the use of the camera system as a microscope with bottom coaxial infrared illumination scheme and demonstrate the ability of through-silicon vision for one-and twolayered silicon photonic integrated circuit samples. The ability of observing objects with dimensions down to 2 µm is confirmed. This resolution is close to diffraction limit and corresponds to the dimensions of optical waveguide structures on the photonic integrated circuits surface. In addition, we demonstrate the implementation of a two-dimensional Fourier transform based autofocusing technique for through-silicon IR microscopy. These building blocks offer a solution for advanced photonic integration processes and other through-silicon vision related applications, which is instrumental for a large variety of assembly, lithography, and wafer bonding setups.

Laser-Assisted Bonding Approach for Photonic Integration Processes

“…The experimental setup consists of a through-silicon machine vision system [12], optically combined with LAB 980 nm laser bottom irradiation beam delivery system (Fig. 2).…”

“…A machine visionbased sensor is employed for non-contact temperature control of the silicon substrate. The temperature data obtained using a through-silicon vision system [12] is compared with data obtained with a pyrometer. For the best temporal resolution, the measurements are taken using a power density of 40 W/cm 2 .…”

“…In this work, we introduce the developed LAB setup with a bottom coaxial irradiation architecture [12], combined with through-silicon machine vision system [12], for obtaining an electrical interconnects (bonds) between pads on a silicon PIC, using pre-dispensed solder paste.…”

A production interface to enable legacy factories for industry 4.0

“…The utilization of computer vision (CV) technologies in manufacturing systems is widespread and serves various purposes, such as enhancing quality control, automating manufacturing tasks, and enabling closed-loop control systems, among others [20,21]. For instance, CV algorithms can be used to detect defects in products and components, such as surface cracks [22], weld defects [23], and misalignments [24]. Monitoring and analyzing sensor data from the production line can enhance quality control and defect prevention [25].…”

“…IoT Real-time monitoring and control, optimization of production schedules [12] Integration challenges with existing manufacturing systems, compatibility issues with legacy equipment and software [15] Predictive maintenance, reducing downtime and saving costs [13] Limited computational resources [15], cybersecurity risks [16] Logistics management, improving productivity and delivery efficiency [14] CV Quality control, defect detection in products and components [22][23][24] Complex deployment, infrastructure requirements [20] Automation of manufacturing tasks, improving efficiency and accuracy [26] Calibration and accuracy validation requirements, limitations in handling complex variations or low-contrast features Human activity observation, facilitating human-machine interaction, ergonomics assessment [27,29] Camera positioning and angle requirements for accurate observation Integration with emerging technologies, enhancing functionality [31][32][33] Additional hardware and software integration requirements, limitations in real-time synchronization and data transfer Machine learning integration, improving accuracy and quality [34,35] Requirements for sufficient training data and computational resources…”