Solinide Raises €4M for AI Data Centre Photonic Chips

Swedish deeptech company Solinide Photonics has raised €4 million in seed funding to commercialise its silicon nitride photonic integrated circuit (PIC) technology. The capital injection is earmarked for scaling manufacturing processes, with the goal of improving performance in AI data centres.

Photonic integrated circuits are recognised as a crucial technology for enabling complex functionalities, offering advantages over traditional microelectronic integrated circuits. Silicon nitride platforms, in particular, are valued for their compatibility with complementary metal-oxide-semiconductor (CMOS) manufacturing infrastructure. This compatibility allows for the potential integration with back-end-of-line Si microelectronics, as highlighted by the IPER-TECH project, which focuses on integrating perovskite LEDs into silicon nitride photonic platforms.

The demand for PIC technology is significant and growing, driven by the need to address challenges such as high power consumption and signal delays in microelectronic ICs. Projects like QPIC1550 further demonstrate the versatility of silicon nitride PICs, aiming to create universal quantum photonic integrated circuit platforms compatible with existing optical fibre networks for applications in quantum communication, computing, and metrology. Solinide's focus on AI data centres leverages these foundational benefits, seeking to deliver more efficient and powerful processing capabilities.

The investment positions Solinide Photonics to advance its proprietary technology from development to market. By targeting AI data centres, the company aims to capitalise on the increasing need for high-speed, low-power data processing solutions. Scalable manufacturing of these advanced photonic chips could significantly impact the infrastructure supporting artificial intelligence.

What this means: Solinide's funding underscores the growing investment interest in deeptech solutions that leverage photonics to overcome the limitations of conventional electronics, particularly in energy-intensive sectors like AI.