Semiconductor Startup Funding Looks To Bounce Back After Lackluster 2023

was the latest chip startup to make headlines when it raised nearly $275 million 鈥� mainly from the German government 鈥� last week for its next-gen chip tech.

It was just the latest sign of chip startups being able to raise big money as once again one of the most foundational technologies grab investors鈥� attention around the world, mainly thanks to AI.

Global venture funding to semiconductor chips appears well on its way to bouncing back this year after a forgettable 2023. Thus far this year, VC-backed chip startups have raised nearly $5.3 billion in just 175 deals, per SA国际传媒 .

Those numbers are well ahead of the pace from last year, when such startups saw less than $8.8 billion in 490 deals. In 2022, chip startups locked up almost $10.9 billion in 447 deals.

More big rounds could be in the way, as it was last week that smartphone-maker is leading a round of at least $300 million for Toronto-based AI chip startup .

US chip boom

U.S. startups are playing a key role in the surge of funding. Domestic startups have raised almost the same amount of money 鈥� about $1.2 billion 鈥� in nearly the same number of deals 鈥� 24 to 22 鈥� compared to all of last year, per .

It is important to note that number is greatly helped out by , which focuses on semiconductor process development and integrated photonic devices and systems. The company landed a financial package of $620 million from the Australian Commonwealth and governments this spring to build a quantum computer at a location in Brisbane, Australia. The round is actually a mix of equity, grants and loans.

Even without that round, U.S. startups would be ahead of last year鈥檚 pace. While many of the biggest rounds this year went to Chinese chipmakers like , and , some large financings also went to U.S.-based semiconductor startups, including:

In March, optical interconnectivity startup raised a massive $175 million Series C led by 鈥檚 . Celestial鈥檚 photonic fabric platform helps separate compute and memory, making processing extensive AI faster and more energy-efficient.
In February, San Jose, California-based , which is developing its AI inference chip for both the generative AI and automotive industries, raised a $102 million Series C co-led by and .
In March, Santa Clara, California-based , a chiplet interconnect developer, raised a $60 million round co-led by and .

鈥淪emi used to be a four-letter word in the Valley, but now it鈥檚 sexy,鈥� said , founding managing partner at San Francisco-based . Along with its Recogni investment, the deep-tech firm鈥檚 portfolio includes Palo Alto, California-based .

AI effect

Of course what is leading to this renewed investor interest is the key driver of so many things in the tech world 鈥� AI.

Artificial intelligence is the driving reason chip giant is now a $3 trillion-plus company. And while shares of 鈥� which provides data and memory connectivity solutions for some of the biggest chipmakers in the world, including and 鈥� are off their highs, they are still well above their IPO price from March. Astera鈥檚 IPO, in particular, was seen as a bellwether offering for both the semiconductor and AI industries.

Both those companies show there is significant public investor interest in the chip market 鈥� and that usually translates to VC interest in the private markets.

鈥淲hile the full promise of AI commercialization has not been fully evidenced, the 鈥楩OMO鈥� of (the) AI race is pushing a lot of hot money into the value chain from AI applications to data infrastructure to semiconductors,鈥� said , founding partner at New York-based , an investor in wireless-device chip manufacturer .

鈥淕iven that at-scale AI application often requires retooling or new build of infrastructure, there is a strong cyclical demand for semis at the moment,鈥� Gu added.

While the space has become more competitive to invest in, it also has become more creative in terms of financing, with more hybrid deals and investors analyzing the risks and capex in more granular details for the industry, Gu said.

Viswanathan added that the semi and hardware space as it relates to AI has been inundated with capital of late and is somewhat 鈥渙ver-inflated.鈥�

Despite the influx of money and investors in the space, Viswanathan said there are opportunities at the silicon and hardware level. That includes startups looking to make AI inference 鈥� a model’s ability to use new data to make predictions and draw conclusions 鈥� more efficient.

However, it is important to remember chipmaking can be an expensive proposition and it is an industry dominated by a few big players like Nvidia.

While those in the AI space may be looking for an alternative to Nvidia, it can be a difficult market for any startups to make headway.

Nevertheless, it seems at least for now investors are willing to take that risk.