Funding & Deals

Myrias Optics Raises US$2.1 Million Seed 1 Round to Scale Nano-Patterned Light-Control Technology

A Massachusetts startup advances scalable light-control tech for AR, AI and imaging markets

Updated

February 27, 2026 3:59 PM

Myrias Optics' Nanoimprinted All-inorganic Metaoptic. PHOTO: MYRIAS OPTICS

Myrias Optics, a Massachusetts-based optical technology startup, has raised US$2.1 million in a Seed 1 financing round to accelerate the commercialization of its advanced light-control technology. The round was led by MassVentures, with participation from existing investors Hoss Investment Inc., Maroon Venture Partners and Tenon Venture Partners, as well as new investors Mill Town Capital, TiE Boston Angels and Doug Crane. This new round follows a US$3.3 million seed financing completed in December 2023, led by Asia Optical, and a US$1.5 million Direct-to-Phase II award from the National Science Foundation. In total, Myrias has secured US$6.9 million to date, positioning it to move from development to scaled production.

The company builds ultra-thin, nano-patterned surfaces that precisely control how light moves through a device. These structures replace or enhance traditional lenses and optical parts inside products such as augmented reality headsets, AI data center hardware, consumer electronics, industrial systems and medical imaging devices. The goal is straightforward: to deliver high optical performance while making the parts easier and more cost-effective to manufacture in large quantities.

Across industries such as augmented reality and AI infrastructure, manufacturers face a common challenge. They need highly precise light-guiding components that can withstand heat and long-term use. At the same time, those components must be produced consistently and at scale. Traditional semiconductor-style fabrication can be costly, while polymer-based optical manufacturing can face limits in durability and thermal stability.

Myrias addresses this gap by using inorganic materials and a nanoimprint manufacturing process to create stable, repeatable optical layers on wafers. This approach is designed to combine performance with manufacturability. In augmented reality systems, for example, the company’s technology enables higher viewing angles while remaining suitable for volume production. In AI data centers, the same material and process advantages support improved light transfer and stronger performance under demanding thermal conditions. These benefits also extend to advanced imaging systems in consumer, industrial and medical markets.

The new Seed 1 funding is intended to expand manufacturing capacity and scale pilot production lines. The company will also continue executing active customer programs. Myrias is already working with strategic partners and Tier 1 supply chain participants to integrate its waveguide and light-shaping solutions into commercial AR platforms, AI photonics systems and advanced imaging products. The capital, therefore, supports a clear next step: moving from validated prototypes to a steady commercial supply.

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M&A & IPOs

Qiming Venture Partners–Backed Axera Goes Public on Hong Kong Stock Exchange

AI’s expansion into the physical world is reshaping what investors choose to back

Updated

February 12, 2026 1:21 PM

Exterior view of the Exchange Square in Central, Hong Kong. PHOTO: UNSPLASH

Artificial intelligence is often discussed in terms of large models trained in distant data centres. Less visible, but increasingly consequential, is the layer of computing that enables machines to interpret and respond to the physical world in real-time. As AI systems move from abstract software into vehicles, cameras and factory equipment, the chips that power on-device decision-making are becoming strategic assets in their own right.

It is within this shift that Axera, a Shanghai-based semiconductor company, began trading on the Hong Kong Stock Exchange on February 10 under the ticker symbol 00600.HK. The company priced its shares at HK$28.2, debuting with a market capitalization of approximately HK$16.6 billion. Its listing marks the first time a Chinese company focused primarily on AI perception and edge inference chips has gone public in the city — a milestone that underscores growing investor interest in the hardware layer of artificial intelligence.

The listing comes at a time when demand for flexible, on-device intelligence is expanding. As manufacturers, automakers and infrastructure operators integrate AI into physical systems, the need for specialized processors capable of handling visual and sensor data efficiently has grown. At the same time, China’s domestic semiconductor industry has faced increasing pressure to build local capabilities across the chip value chain. Companies such as Axera sit at the intersection of these dynamics, serving both commercial markets and broader industrial policy priorities.

For Hong Kong, the debut adds to a cohort of technology companies seeking public capital to scale hardware-intensive businesses. Unlike software firms, semiconductor designers operate in a capital-intensive environment shaped by supply chains, fabrication partnerships and rapid product cycles. Their presence on the exchange reflects a maturing investor appetite for AI infrastructure, not just consumer-facing applications.

Axera’s early backer, Qiming Venture Partners, led the company’s pre-A financing round in 2020 and continued to participate in subsequent rounds. Prior to the IPO, it held more than 6 percent of the company, making it the second-largest institutional investor. The public offering provides liquidity for early investors and new funding for a company operating in a highly competitive and technologically demanding sector.

Axera’s market debut does not resolve the competitive challenges of the semiconductor industry, where innovation cycles are short and global competition is intense. But it does signal that investors are placing tangible value on the hardware, enabling AI’s expansion beyond the cloud. In that sense, the listing represents more than a corporate milestone; it reflects a broader transition in how artificial intelligence is built, deployed and financed — moving steadily from software abstraction toward the silicon that makes real-world autonomy possible.