Applied Materials, Inc.

Zhengping Yao

Ludovic Godet

Daihua Zhang

Yingdong Luo

Xiaopei Deng

Todd J Egan

Samarth Bhargava

Kevin Messer

Lei Lian

Takashi Kuratomi

Pengyu Han

Paul J Steffas

David Alexander Sell

Barry Craver

Chao-Hsien Lee

Jinyu Lu

Jingyi Yang

Alexia Adilene Portillo Rivera

Pin Ham Lu

Wan Hsueh Lai

Shu Yu Chen

Embodiments of the present disclosure generally relate to methods for forming a waveguide. Methods may include measuring a waveguide substrate, the waveguide having a substrate thickness distribution; and depositing an index-matched layer onto a surface of the waveguide, the index-matched layer having a first surface disposed on the waveguide substrate and a second surface opposing the first surface, wherein the index-matched layer is disposed only over a portion of the waveguide substrate, and a device slope of a second surface of the index-matched layer is substantially the same as the waveguide slope of the first surface of the waveguide.

Total or local thickness variation for optical devices

Embodiments of the present disclosure generally relate to optical devices. More specifically, embodiments described herein relate to optical devices and methods of manufacturing a patterned optical device film on an optical device substrate. According to certain embodiments, an inkjet deposition process is used to deposit a patterned inkjet coating layer on the optical device substrate. A deposition process may then be used to deposit an optical device material on the patterned inkjet coating and the optical device substrate. The patterned inkjet coating on the optical device substrate may then be washed with an appropriate detergent to lift-off the patterned inkjet coating layer from the optical device substrate to form the patterned optical device film.

Patterning of optical device films via inkjet soluble mask, deposition, and lift-off

Methods and systems for detection of an endpoint of a substrate process are provided. A set of machine learning models are trained to provide a metrology measurement value associated with a particular type of metrology measurement for a substrate based on spectral data collected for the substrate. A respective machine learning model is selected to be applied to future spectral data collected during a future substrate process for a future substrate in view of a performance rating associated with the particular type of metrology measurement. Current spectral data is collected during a current process for a current substrate and provided as input to the respective machine learning model. An indication of a respective metrology measurement value corresponding to the current substrate is extracted from one or more outputs of the trained machine learning model. In response to a determination that the respective metrology measurement satisfies a metrology measurement criterion, an instruction including a command to terminate the current process is generated.

Substrate process endpoint detection using machine learning

An additive manufacturing apparatus includes a platform, a dispenser configured to deliver a plurality of successive layers of feed material on a platform, a light source configured to generate a light beam, an auxiliary polygon mirror scanner configured to receive the light beam from the light source and reflect the light beam, and a primary mirror scanner to receive the light beam reflected by the auxiliary polygon mirror scanner and direct the light beam to impinge on an exposed layer of feed material.

Additive manufacturing with dithering scan path

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Cupertino, CA, United States of America

Zhengping Yao