Transitioning Beyond Transistors

Computing at the speed of light

Spring 2024
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Mohammed Hassan

Mohammed Hassan

Photo: Amee Hennig

Mohammed Hassan, associate professor in the College of Science, leads international research demonstrating a way to register the on/off switching of laser signals at speeds on the scale of attoseconds, or quintillionths of a second. 

The breakthrough paves the way for previously unattainable data transfers, including ultra-distance communications, such as from Earth into deep space. 

Nearly all computers and electronics in use today still rely on semiconductor-based transistors, a 1940s innovation that translates electrical signals into “on or off” binary data. Efforts to advance computing power have overwhelmingly focused on increasing the rate of signaling, achieving speeds at the scale of trillionths of a second in today’s most advanced systems.

 However, the electricity in these systems creates heat in various ways, requiring cooling strategies like fans or liquid cooling systems. This also establishes a theoretical ceiling for performance because at some point, the energy required for cooling the system exceeds the energy it can support. 

As an alternative to electricity, all-optical signaling offers a way around the heat problem. It also enables data transfer a million times faster. But registering the signals to translate them into binary data has been an unsolved challenge. 

Hassan and his research team devised a way to log the on/off state of laser signals at unprecedented speeds with fused silica. This special form of silicon dioxide can change from being reflective to being nearly transparent — corresponding to the on/off state of computing data — almost instantaneously.


Data Connects Us

The Data Connects Us series is published in partnership with the university's Office of Research, Innovation & Impact (RII) and the Institute for Computation & Data-Enabled Insight. RII content was written by Eric Van Meter and originally published in the RII Magazine in December 2023 based on reporting by Rosemary Brandt, Logan Burtch-Buus, Anna Christensen, Emily Dieckman, Mikayla Mace Kelley, Susan McGinley, Kimberly Nichols and Niranjana Rajalakshmi.

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