Researchers at Stanford are advancing a new class of nonlinear optical devices that operate with significantly lower energy requirements than previous platforms.
Researchers at Stanford University have designed a scalable photonic quantum computer which does not require single-photon detectors and which uses minimal quantum resources: one coherently controlled atom.
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 at Stanford have demonstrated a new type of energy-efficient and ultrathin memory. This low-energy cost memory is based on stacking orders in the atomically thin limit, associated with tiny changes in the position of one atomic layer with respect to another.
Researchers at Stanford have developed a non-destructive method for generating and patterning optical color centers with nanoscale resolution without the need for high energy radiation. Color centers, which are optically active defects within the lattice structur
A team of Stanford engineers have developed an accurate, robust location-based security method using signals from distinct classes of communication systems.
Machine learning models currently require extensive computational resources and this demand is growing rapidly with new models and applications being introduced.
Stanford has patented a fully homomorphic encryption (FHE) method, computer program, and apparatus that grant the ability to outsource data processing without giving away access to the data.
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.
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.
Stanford researchers at the Vuckovic Lab have created a computational nanophotonic design library for gradient-based optimization called the Stanford Photonic INverse design Software (Spins).
Researchers at Stanford have developed a structure for a Low-Threshold Germanium laser that is easily integrable into electronic and photonic circuits, and competitive with current state-of-the-art III-V lasers.