University of California

California Institute of Technology

Hewlett-packard Development Company, L.P.

Yi Luo

Aydogan Ozcan

James R Heath

Yair Rivenson

Deniz Mengu

Xing Lin

Patricia A Beck

Charles Patrick Collier

Rob Beckman

Erica DeIonno

Jingxi Li

A diffractive optical neural network device includes a plurality of diffractive substrate layers arranged in an optical path. The substrate layers are formed with physical features across surfaces thereof that collectively define a trained mapping function between an optical input and an optical output. A plurality of groups of optical sensors are configured to sense and detect the optical output, wherein each group of optical sensors has at least one optical sensor configured to capture a positive signal from the optical output and at least one optical sensor configured to capture a negative signal from the optical output. Circuitry and/or computer software receives signals or data from the optical sensors and identifies a group of optical sensors in which a normalized differential signal calculated from the positive and negative optical sensors within each group is the largest or the smallest of among all the groups.

Diffractive deep neural networks with differential and class-specific detection

An all-optical Diffractive Deep Neural Network (DNN) architecture learns to implement various functions or tasks after deep learning-based design of the passive diffractive or reflective substrate layers that work collectively to perform the desired function or task. This architecture was successfully confirmed experimentally by creating 3D-printed DNNs that learned to implement handwritten classifications and lens function at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using DNNs. In alternative embodiments, the all-optical DNN is used as a front-end in conjunction with a trained, digital neural network back-end.

Devices and methods employing optical-based machine learning using diffractive deep neural networks

A system and method for selecting nanometer-scaled devices. The method includes a plurality of semiconductor wires. Two adjacent semiconductor wires of the plurality of semiconductor wires are associated with a separation smaller than or equal to 100 nm. Additionally, the system includes a plurality of address lines. Each of the plurality of address lines includes a gate region and an inactive region and intersects the plurality of semiconductor wires at a plurality of intersections. The plurality of intersections includes a first intersection and second intersection. The first intersection is associated with the gate region, and the second intersection is associated with the inactive region.

System and method based on field-effect transistors for addressing nanometer-scale devices

Method for making a system for selecting one wire from a plurality of wires

Methods for making electronic devices where a molecular monolayer or multilayer is sandwiched between top and bottom electrodes at electrode intersections. The molecular layer has an electrical characteristic such as bistable switching. A layer of electrically conductive material is used to protect the molecular layer during formation of the top electrode pattern. The electrically conductive material remains sandwiched between the top and bottom electrodes at the electrode intersections in the final electronic device.

Method for lithographic processing on molecular monolayer and multilayer thin films

Growing community of inventors

Los Angeles, CA, United States of America

Yi Luo