Photonic chips have revolutionized data-heavy technologies. On their own or in concert with traditional electronic circuits, these laser-powered devices send and process information at the speed of light, making them a promising solution for artificial intelligence’s data-hungry applications.
In addition to their incomparable speed, photonic circuits use significantly less energy than electronic ones. Electrons move relatively slowly through hardware, colliding with other particles and generating heat, while photons flow without losing energy, generating no heat at all. Unburdened by the energy loss inherent in electronics, integrated photonics are poised to play a leading role in sustainable computing.
Photonics and electronics rely on a technique similar to stenciling known as lithography, wherein intense light passes through a patterned mask and transfers a circuit design onto a semiconductor wafer by selectively exposing and removing a photosensitive material to define their circuit elements and connect them sequentially.
While photonic chips don’t use the transistors that populate electronic chips’ ever-shrinking and increasingly layered grooves, their complex lithographic patterning guides laser beams through a coherent circuit to form a photonic network that can perform computational algorithms.
Now, researchers at the School of Engineering and Applied Science have created a novel photonic device that provides programmable on-chip information processing without lithography, offering the speed of photonics augmented by superior accuracy and flexibility for AI applications.
Achieving unparalleled control of light, this device consists of spatially distributed optical gain and loss. Lasers cast light directly on a semiconductor wafer, without the need for defined lithographic pathways.
Liang Feng, a professor in the Departments of Materials Science and Engineering and Electrical Systems and Engineering, along with Ph.D. student Tianwei Wu and postdoctoral fellows Zihe Gao and Marco Menarini, introduced the microchip in a study published in Nature Photonics.
“But photonic chips intended for machine learning applications face the obstacles of an intricate fabrication process where lithographic patterning is fixed, limited in reprogrammability, subject to error or damage and expensive,” says Feng. “By removing the need for lithography, we are creating a new paradigm. Our chip overcomes those obstacles and offers improved accuracy and ultimate reconfigurability given the elimination of all kinds of constraints from predefined features.”
This story is by Devorah Fischler. Read more at Penn Engineering Today.
