Semiconductors Startups funded by Y Combinator (YC) 2026

Spectre Intelligence is a neotrading firm. We create AI traders instead of hiring human ones. Currently trading semis + bio.

We are building the future of chip manufacturing. Chips are more advanced than ever, and the features inside them keep getting smaller. But the cleanroom infrastructure supporting their manufacture has not scaled at the same pace. Intel, TSMC, Samsung, and others spend billions building and operating massive cleanrooms, only to isolate their most advanced wafers from the surrounding facility as much as possible. We think that architecture is backwards. Instead of making the entire building cleaner, why not make the machinery itself as clean as possible? Talos is our proprietary tool-integrated purification system. It sits directly inside semiconductor equipment and continuously destroys airborne molecular contaminants before they reach the wafer. Instead of fighting contamination by constantly flooding tools with ultra-high-purity nitrogen, Talos keeps the environment inside the machine 10–100x cleaner, without the massive purge-gas bill. Talos is powerful on its own, but it is also the foundation for a much bigger vision: the Cleanpipe. The Aerogen Systems Cleanpipe is an ultra-clean interconnect infrastructure that protects wafers as they move from machine to machine across the fab. Rather than relying on a billion-dollar cleanroom to protect every cubic foot of factory space, Cleanpipe creates a controlled environment only where cleanliness actually matters: around the wafer. Talos cleans the tools. Cleanpipe connects them. Eventually, we delete the cleanroom and build more advanced chips, at greater scale, with radically less infrastructure. With cleanpipes connecting tools, chip fabs can exist anywhere.

We build environments and evaluations to train and evaluate models on advanced chip design.

Lamb Labs is building MPUs (Model Processing Units), custom chips that hardcode LLM weights directly into the chip. Unlike GPUs, which move weights between memory and compute, MPUs keep the model on-chip, targeting up to 20,000+ tok/s and a 63× higher intelligence per watt.

Hardware Intelligence makes chip design tools that cut simulation and debugging time by an order of magnitude. When a chip test fails, engineers need a recording of every internal signal - a waveform. We’ve developed WaveZip, which captures waveforms 500x smaller with 200x less overhead, and Wave, which beats frontier models on industry chip debugging benchmarks. We’re Rishov Sarkar (Georgia Tech Ph.D. in chip simulation) and Athreya Anand (ex-Google, M.S. ML/AI), YC S26. https://hardwareintelligence.ai

Simantic is modernizing the embedded firmware industry with hardware simulation developed for the adoption of AI in hardware engineering. Simantic gives both humans and AI the visibility into hardware previously limited by the physical world.

We use AI to discover new materials for the semiconductor industry - specifically datacenters and fabs. Finding novel materials today takes 10+ years of lab work. We aim to compress that timeline to months, using a swarm of AI agents. Akash completed his PhD at Stanford on material discovery for semiconductors. The materials he discovered for nanoscale interconnects have been adopted into the roadmaps of Intel and TSMC. Advaith was the founding applied scientist at Persona AI (acquired by Luma Labs), where he built long horizon autonomous agents for major telecom and crypto companies. Power consumption and heat release from AI chips is doubling every year. The semiconductor industry needs new materials to keep this exponential going - Discovered Materials will help find them.

Visibl Semiconductors makes custom silicon accessible to hardware companies, starting with power electronics. We are an AI-native custom-silicon design partner that cuts ASIC development cost and timeline on mature nodes, helping teams deliver better performance and lower BOM cost. We work with data-center and AI-infrastructure power, motor drives, industrial automation and robotics, industrial power supplies and conversion, battery systems and e-mobility powertrains, EV charging, solar, energy storage, grid power, HVAC, heat pumps, and appliances. V1 boards built from off-the-shelf parts are great for getting to market quickly, but scaling to v2+ volume requires optimizing cost, size, and manufacturability. Visibl folds those parts into custom silicon built around the product: a simpler BOM, reclaimed board space, fewer supply constraints, and automation-ready assembly. We manage the full path from architecture and design through verification, fabrication, packaging, and production-ready silicon, working alongside hardware teams so they do not need an in-house chip team.

Inversion Semiconductor is developing the next-generation chip fabrication machine, to create the most powerful chips 15x faster. Our vision is to reshore advanced chip fabrication capabilities in the West. We're building a lithography machine. Lithography uses light to pattern circuit features on silicon. We're scaling transistors to their physical limits by shrinking particle accelerators 1000x, to create a high power light source. Using our light source we will double transistor density for a given numerical aperture and 3x throughput.

Mobius is the safe spot market for electronic components like microcontrollers and capacitors. We help electronics manufacturers buy and sell chips quickly, safely & at market prices.

DeepSim is building an AI physics simulator. We are currently developing the only thermal simulator to meet AI chip design needs and are validating our tool with Intel. We are a team of three electrical engineering PhDs from Stanford with backgrounds in semiconductor fabrication and design.

AI silicon for the era of recursive self-improvement and superintelligence

Building out new LNG plants, HVAC systems, or semiconductor processes rely on hundreds of iterations of feasibility testing (through simulation or real-world lab tests) where different designs (combinations of equipment) and parameters are validated. The parameters are often locked in PDFs (datasheets, wiring diagrams, PFDs/P&IDs) which take thousands of hours to convert into CSVs / JSONs, and the simulators don't have an easy API to work with. Outerport bridges the gap by finding documents from PLMs, extracting structured data from drawings, building a knowledge graph over them, and building autonomous AI agents that can fully automate this R&D process by running simulations and performing design checks.

Conductor Quantum is building quantum superintelligence: AI that operates quantum computers to make scientific discoveries beyond human reach. Quantum computers let us understand the world at its most fundamental level, the path to new drugs, new materials, and discoveries no human can reach alone. A quantum computer is the perfect simulator of nature: it encodes the logic of reality into a programmable machine, atom by atom, electron by electron. Give AI that simulator and you open the door to discovery. The bottleneck is operating the hardware. Today, engineers spend days or weeks by hand to bring a chip to operating conditions for just two qubits. A qubit is the information-carrying unit of a quantum computer, the equivalent of a bit in a classical one, and a useful machine needs billions. Removing the human from that loop is only half the problem. Every command an AI sends to a quantum computer must be optimised for the specific hardware it runs on. We build the AI that operates quantum computers and tunes every machine it runs on. That is the path to quantum superintelligence.

Silogy is building an AI-powered platform for chip developers to simulate and debug their designs.

We're building AI-native tools & agent-driven workflows for frontend chip design. Today, Fortune enterprises and chip unicorns license Silimate to hit their PPA targets and functional closure in days instead of months. With Silimate, chip companies tape out better, more market-competitive chips much more quickly.

Novel AI hardware built from first principles to let companies train, fine-tune, and deploy frontier models with orders of magnitude better performance than incumbents

Bronco is an applied AI lab helping chipmakers keep Moore’s law going. Our mission is to build AI silicon verification engineers that find bugs, drive functional coverage, and help companies ship working chips on time. We're currently deployed alongside verification teams at leading chip companies building next generation hardware (4 nm process node). We are well-funded and backed by tier-1 investors, including YC, 8VC, Elad Gil, SV Angel, and Contrary Capital. We have also established strong relationships with the leading academic and industry researchers in the field of AI for chip design. Our advisors have won multiple Best Paper Awards at leading conferences, been cited tens of thousands of times, and received some of the highest honors in the microelectronics and EDA industries.