UCLA Shrinks Terahertz Systems Onto Single Semiconductor Chip

UCLA engineers have integrated the core functions of terahertz systems, generation, detection, modulation, and amplification, onto a single semiconductor chip, potentially unlocking widespread applications for this underused portion of the electromagnetic spectrum. High-frequency terahertz waves, positioned between infrared light and microwaves, hold promise for ultrafast wireless communication, security screening, and advanced imaging, but have been hampered by bulky and complex systems. The UCLA Samueli School of Engineering team achieved this miniaturization through the use of quantum well semiconductor structures, ultrathin layers engineered to control light. “Terahertz optoelectronic systems have been bulky, expensive, power-hungry and difficult to scale for widespread use,” said study leader Mona Jarrahi, a professor of electrical and computer engineering and holder of UCLA Samueli’s Northrop Grumman Chair in Electrical Engineering. “By demonstrating that many of these functions can be integrated onto a single chip using proven industry-standard fabrication platforms, our study opens the door to practical, scalable terahertz technologies for real-world applications.”

Researchers at the UCLA Samueli School of Engineering have successfully integrated these functions, overcoming the limitations of bulky, discrete components that have historically hampered widespread terahertz technology adoption. The team’s innovation centers on adapting quantum wells, already commonplace in photonic integrated circuits, to support terahertz signal generation and detection through a process called gain-enhanced interband photomixing. This technique combines two laser beams to create signals at a specific wavelength, offering a pathway to efficient terahertz production. Unlike earlier single-chip terahertz systems that relied on specialized fabrication techniques, the UCLA approach leverages existing industry-standard platforms, promising scalability and cost-effectiveness. The researchers demonstrated both efficient terahertz generation and sensitive detection using their quantum well substrates within photonic integrated circuits, surpassing the performance of existing light interference-based terahertz technologies. This advancement is particularly significant given the growing demand for photonics-based terahertz systems, which offer superior bandwidth and power efficiency compared to conventional electronics.

Terahertz optoelectronic systems have been bulky, expensive, power-hungry and difficult to scale for widespread use.

This achievement moves beyond simply miniaturizing existing components; it reimagines how these functions are created using established industry practices. The team’s approach leverages the existing infrastructure of photonic integrated circuits, mirroring the transformation of computers from room-sized machines to modern microprocessors. By demonstrating compatibility with standard fabrication platforms, the researchers suggest a pathway toward mass-producible terahertz chips for real-world applications.

By demonstrating that many of these functions can be integrated onto a single chip using proven industry-standard fabrication platforms, our study opens the door to practical, scalable terahertz technologies for real-world applications.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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