Stanford researchers at Prof. Safavi-Naeini's laboratory have developed a high quality, scalable processor architecture using small, phononic crystal resonators for read-out and long-lived storage in superconducting circuit quantum computing.
Researchers in the Dionne group at Stanford have designed a nanoscale laser capable of self-isolated Raman Lasing, where lasing and isolation occurs within the same pumping mechanism.
Stanford researchers have developed a simple optical device for low-power, active light tuning. The device tunes the color of light across the visible spectrum and at select wavelengths by electrical biasing an array of micron sized pixels or nanowires.
Researchers in the Arbabian Lab have developed a system that uses a combination of radio frequency (RF) electromagnetic and ultrasound (US) waves to detect, localize, and identify multiple battery-free tags.
Stanford researchers have proposed two learning techniques for MIMO secondary users (SU) to spatially coexist with Primary Users (PU). Today, most of the spectrum is allocated to primary users for exclusive use.
Stanford researchers developed a method to make large phase shifts with little or no power dissipation in integrated optics. The approach uses a directional coupler moved by a MEMS actuator to achieve a path delay, i.e. an effective change in refractive index.
To significantly reduce the energy consumed by mobile web browsers, a system was developed for precise measurement of power consumption by browsers of mobile devices when rendering web pages.
Mobile devices often connect to the network via wireless channels. In general, the downlink of the wireless channel (e.g., the cellular access network) is limited in throughput.
Stanford researchers have developed an optical coating that steers infrared and visual light in different paths while suppressing the typical undesired rainbow effect.
Stanford researchers have invented a C-Aperture Nano-Tip which provides a new way to further enhance the optical resolution down to smaller than 15 nm.
Engineers in Prof. Shanhui Fan's laboratory have developed an efficient, scalable, in-situ method to train, configure and tune complex photonic circuits for artificial intelligence and machine learning.