Penn Engineers have created the first programmable photonic chip that can train nonlinear neural networks using light, potentially revolutionizing AI by making it faster and more energy-efficient. Unlike traditional electronic chips, this new chip reshapes light itself to perform complex computations, enabling real-time learning and offering a major step toward fully light-powered computers.

Breakthrough light-powered chip speeds up AI training and reduces energy consumption.

Engineers at Penn have developed the first programmable chip capable of training nonlinear neural networks using light—a major breakthrough that could significantly accelerate AI training, lower energy consumption, and potentially lead to fully light-powered computing systems.

Unlike conventional AI chips that rely on electricity, this new chip is photonic, meaning it performs calculations using beams of light. Published in

Postdoctoral fellow Tianwei Wu (left) and Professor Liang Feng (right) in the lab, demonstrating some of the apparatus used to develop the new, light-powered chip. Credit: Sylvia Zhang

Without that nonlinearity, adding layers does nothing: the system just reduces to a single-layer linear operation, where inputs are simply added together, and no real learning occurs.

While many research teams, including teams at Penn Engineering, have developed light-powered chips capable of handling linear mathematical operations, none has solved the challenge of representing nonlinear functions using only light — until now.

“Without nonlinear functions, photonic chips can’t train deep networks or perform truly intelligent tasks,” says Tianwei Wu (Gr’24), a postdoctoral fellow in ESE and the paper’s first author.

Reshaping Light with Light

The team’s breakthrough begins with a special semiconductor material that responds to light. When a beam of “signal” light (carrying the input data) passes through the material, a second “pump” beam shines in from above, adjusting how the material reacts.

By changing the shape and intensity of the pump beam, the team can control how the signal light is absorbed, transmitted, or amplified, depending on its intensity and the material’s behavior. This process “programs” the chip to perform different nonlinear functions.

“We’re not changing the chip’s structure,” says Feng. “We’re using light itself to create patterns inside the material, which then reshapes how the light moves through it.”

The result is a reconfigurable system that can express a wide range of mathematical functions depending on the pump pattern. That flexibility allows the chip to learn in real time, adjusting its behavior based on feedback from its output.

Training at the Speed of Light

To test the chip’s potential, the team used the chip to solve benchmark AI problems. The platform achieved over 97%

An image of the light inside the chip — the white dashed boxes are the inputs and the yellow dashed boxes the outputs. Credit: Liang Feng, Tianwei Wu

In one striking result, just four nonlinear optical connections on the chip were equivalent to 20 linear electronic connections with fixed nonlinear activation functions in a traditional model. That efficiency hints at what’s possible as the architecture scales.

Unlike previous photonic systems, which are fixed after fabrication, the Penn chip starts as a blank canvas. The pump light acts like a brush, drawing reprogrammable instructions into the material.

“This is a true proof-of-concept for a field-programmable photonic computer,” says Feng. “It’s a step toward a future where we can train AI at the speed of light.”

Future Directions

While the current work focuses on polynomials — a flexible family of functions widely used in machine learning — the team believes their approach could enable even more powerful operations in the future, such as exponential or inverse functions. That would pave the way for photonic systems that tackle large-scale tasks like training large language models.

By replacing heat-generating electronics with low-energy optical components, the platform also promises to slash energy consumption in AI data centers, potentially transforming the economics of machine learning.

“This could be the beginning of photonic computing as a serious alternative to electronics,” says Liang. “Penn is the birthplace of ENIAC, the world’s first digital computer — this chip might be the first real step toward a photonic ENIAC.”

Reference: “Field-programmable photonic nonlinearity” by Tianwei Wu, Yankun Li, Li Ge and Liang Feng, 15 April 2025, Nature Photonics.
DOI: 10.1038/s41566-025-01660-x

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