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A Single Chip Could Transform the Future of Photonic Computing

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August 26, 2025
in Science
A Single Chip Could Transform the Future of Photonic Computing


One chip may replace a tangled web of electronics. Researchers at Harvard’s John A. Paulson School of Engineering and Applied Sciences have created a lithium niobate device that directly converts digital electronic signals into analog light waves. Published in Nature Photonics, the work demonstrates a faster, more energy-efficient interface between electronics and photonics, with potential to accelerate data centers, fiber-optic networks, and AI systems.

Breaking the Bottleneck

Modern transceivers convert digital signals into analog optical ones through a two-step process: digital-to-analog converters (DACs) followed by electro-optic modulators. While effective, this system consumes considerable power and slows down photonic computing. The new electro-optic digital-to-analog link (EO-DiAL) condenses both steps into one.

Built from thin-film lithium niobate, the device achieves transmission rates of up to 186 gigabits per second—roughly an order of magnitude beyond typical home internet speeds. It does so with energy efficiency that could reshape computing hardware.

“For photonic technologies to seamlessly integrate with electronic ones, the interfaces between them must be fast and energy-efficient,” said Marko Loncar, Tiantsai Lin Professor of Electrical Engineering at Harvard SEAS.

How It Works

The EO-DiAL relies on a multi-electrode interferometer design, which translates binary inputs directly into multi-level optical signals. This allows binary ones and zeros to become complex waveforms—sine, square, or otherwise—in a single step. When tested, the chip could encode benchmark images from the MNIST dataset with 95 percent fidelity.

By combining its optical output with photodetectors, the system can also generate microwave waveforms, opening potential uses in wireless communications, radar, and radio frequency technologies. Unlike conventional approaches, it accomplishes this without bulky and inefficient pulse-shaping electronics.

Made for Scalability

A key achievement lies in manufacturability. The Harvard team fabricated the EO-DiAL using a mature lithium niobate foundry process developed by startup HyperLight Corporation. Much like the silicon chip ecosystem, this process enables wafer-scale production at low cost, supporting integration with existing photonics and electronics.

Key Findings

  • Device: Electro-optic digital-to-analog link (EO-DiAL) built on thin-film lithium niobate
  • Speed: Operates up to 186 Gb/s with 4-bit resolution
  • Energy efficiency: 0.058 pJ/bit, significantly lower than conventional systems
  • Applications: Photonic computing, fiber-optic communications, microwave photonics
  • Testing: Demonstrated high-fidelity encoding of MNIST dataset images
  • Fabrication: Wafer-scale process via HyperLight, scalable like silicon photonics
  • Collaborators: Harvard SEAS, Peking University, HyperLight, Wellman Center of Photomedicine, University of Singapore

Implications for Computing and Communications

The rise of artificial intelligence, large language models, and high-capacity data centers has highlighted the limits of electronic infrastructure. Optical computing offers parallelism and speed, but only if digital and analog domains can be bridged efficiently. By eliminating one of the largest energy bottlenecks, the EO-DiAL brings that vision closer.

Beyond AI and photonic processors, the device could find use in radar, LiDAR, and quantum technologies, where high-speed waveform generation is critical. Researchers believe the platform can scale to higher bit resolutions and integrate with chip-scale lasers and detectors, further reducing cost and footprint.

Takeaway

Harvard engineers have demonstrated a lithium niobate chip that converts digital signals directly into analog light in one step. The advance simplifies optical systems, reduces energy costs, and could accelerate photonic computing, fiber-optic communications, and next-generation AI hardware.

Journal: Nature Photonics
DOI: 10.1038/s41566-025-01719-9

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