22 Apr 2021
Silicon nitride-based PICs offer “record low optical losses” as well as smaller footprints.
Integrated photonic chips are usually made from silicon that is abundant and has good optical properties. But silicon cannot perform every required function in integrated photonics, so new material platforms have emerged.
One of these is silicon nitride (Si3N4), whose exceptionally low optical loss – orders of magnitude lower than that of silicon – has made it the material of choice for applications for which low loss is critical, such as narrow-linewidth lasers, photonic delay lines, and nonlinear photonics.
Now, scientists in the group of Professor Tobias Kippenberg at EPFL’s School of Basic Sciences have developed a new technology for producing silicon nitride integrated photonic circuits (“PICs”) with record low optical losses and small footprints. The work is published in Nature Communications.
Combining nanofabrication and material science, the technology is based on the photonic Damascene process developed at EPFL. Using this process, the team made integrated circuits of optical losses of only 1 dB/m, a record value for any nonlinear integrated photonic material.
Such low loss significantly reduces the power budget for building chip-scale optical frequency combs (“microcombs”), used in applications like coherent optical transceivers, low-noise microwave synthesizers, LiDAR, neuromorphic computing, and even optical atomic clocks.
New apps: LiDAR, neural networks, quantum computing
The team used the new technology to develop meter-long waveguides on 5×5 mm2 chips and high-quality-factor microresonators. They also report high fabrication yield, which is essential for scaling up to industrial production.
“These chip devices have already been used for parametric optical amplifiers, narrow-linewidth lasers and chip-scale frequency combs,” saidDr. Junqiu Liu who led the fabrication at EPFL’s Center of MicroNanoTechnology (CMi).
“We are also looking forward to seeing our technology being used for emerging applications such as coherent LiDAR, photonic neural networks, and quantum computing.”
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