Massachusetts Institute of Technology

Mihika Prabhu

Marin Soljacic

Dirk Robert Englund

Scott A Skirlo

Nicholas Christopher Harris

Yichen Shen

Jacques Johannes Carolan

Simon Verghese

Jeffrey Scott Herd

Paul William Juodawlkis

Cheryl Marie Sorace-Agaskar

Yi Yang

An optical neural network is constructed based on photonic integrated circuits to perform neuromorphic computing. In the optical neural network, matrix multiplication is implemented using one or more optical interference units, which can apply an arbitrary weighting matrix multiplication to an array of input optical signals. Nonlinear activation is realized by an optical nonlinearity unit, which can be based on nonlinear optical effects, such as saturable absorption. These calculations are implemented optically, thereby resulting in high calculation speeds and low power consumption in the optical neural network.

Apparatus and methods for optical neural network

An integrated optical beam steering device includes a planar dielectric lens that collimates beams from different inputs in different directions within the lens plane. It also includes an output coupler, such as a grating or photonic crystal, that guides the collimated beams in different directions out of the lens plane. A switch matrix controls which input port is illuminated and hence the in-plane propagation direction of the collimated beam. And a tunable light source changes the wavelength to control the angle at which the collimated beam leaves the plane of the substrate. The device is very efficient, in part because the input port (and thus in-plane propagation direction) can be changed by actuating only logN of the N switches in the switch matrix. It can also be much simpler, smaller, and cheaper because it needs fewer control lines than a conventional optical phased array with the same resolution.

Methods and systems for optical beam steering

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Cambridge, MA, United States of America

Mihika Prabhu