A problem with current food service robots is making the robots safe to work around food. A solution provided by the present disclosure is a food-safe tool switcher and corresponding tool. The tool switcher can mate with a variety of tools, which can be molded or 3D printed out of food-safe materials into a single-part, instead of constructed modularly. This provides for easier cleaning.

In an embodiment, a cover for an automated robot includes elastic sheets that are adhered to each other in a geometry. The geometry is configured to allow the elastic sheets to expand and contract while the automated robot moves within its range of motion. The elastic sheets are attached to the automated robot by elasticity of the elastic sheets. A first group of the elastic sheets forms an elastic collar configured to grip the automated robot at a distal end and a proximal end of the cover. A person of ordinary skill in the art can recognize that nonbreakably means that during operation of the robot, the elastic sheets hold their elasticity and integrity without breaking.

In an embodiment, a method for handling an order includes determining a plurality of ingredients based on an order, received from a user over a network, for a location having a plurality of robots. The method further includes planning at least one trajectory for at least one robot based on the plurality of ingredients and utensils available at the location, and proximity of each ingredient and utensil to the at least one robot. Each trajectory can be configured to move one of the plurality of ingredients into a container associated with the order. In an embodiment, the method includes executing the at least one trajectory by the at least one robot to fulfill the order. In an embodiment, the method includes moving the container to a pickup area.

In an embodiment, a method includes determining a given material to manipulate to achieve a goal state. The goal state can be one or more deformable or granular materials in a particular arrangement. The method further includes, for the given material, determining, a respective outcome for each of a plurality of candidate actions to manipulate the given material. The determining can be performed with a physics-based model, in one embodiment. The method further can include determining a given action of the candidate actions, where the outcome of the given action reaching the goal state is within at least one tolerance. The method further includes, based on a selected action of the given actions, generating a first motion plan for the selected action.

A system for characterizing a specimen is disclosed. In one embodiment, the system includes a characterization sub-system configured to acquire one or more images a specimen, and a controller communicatively coupled to the characterization sub-system. The controller may be configured to: receive training images of one or more features of a specimen from the characterization sub-system; receive training three-dimensional (3D) design images corresponding to the one or more features of the specimen; generate a deep learning predictive model based on the training images and the training 3D design images; receive product 3D design images of one or more features of a specimen; generate simulated images of the one or more features of the specimen based on the product 3D design images with the deep learning predictive model; and determine one or more characteristics of the specimen based on the one or more simulated images.

Implementations described herein generally relate to a method for detecting anomalies in time-series traces received from sensors of manufacturing tools. A server feeds a set of training time-series traces to a neural network configured to derive a model of the training time-series traces that minimizes reconstruction error of the training time-series traces. The server extracts a set of input time-series traces from one or more sensors associated with one or more manufacturing tools configured to produce a silicon substrate. The server feeds the set of input time-series traces to the trained neural network to produce a set of output time series traces reconstructed based on the model. The server calculates a mean square error between a first input time series trace of the set of input time series traces and a corresponding first output time series trace of the set of output time-series traces. The server declares the sensor corresponding to the first input time-series trace as having an anomaly when the mean square error exceeds a pre-determined value.

An artificial intelligence server for controlling a plurality of robots using artificial intelligence includes a communication unit configured to receive a captured image of each of a plurality of zones and a processor configured to acquire situation information of each zone based on the received image, acquire degrees of guidance urgency respectively corresponding to the plurality of zones based on the acquired situation information of each zone, determine whether there is a degree of guidance urgency greater than a predetermined value in the acquired degrees of guidance urgency, and, when there is a degree of guidance urgency greater than the predetermined value, transmit a first control command for moving one or more robots to a zone corresponding to the degree of guidance urgency.

A method of generating training spectra for training of a neural network includes measuring a first plurality of training spectra from one or more sample substrates, measuring a characterizing value for each training spectra of the plurality of training spectra to generate a plurality of characterizing values with each training spectrum having an associated characterizing value, measuring a plurality of dummy spectra during processing of one or more dummy substrates, and generating a second plurality of training spectra by combining the first plurality of training spectra and the plurality of dummy spectra, there being a greater number of spectra in the second plurality of training spectra than in the first plurality of training spectra. Each training spectrum of the second plurality of training spectra having an associated characterizing value.

A method of generating training spectra for training of a neural network includes generating a plurality of theoretically generated initial spectra from an optical model, sending the plurality of theoretically generated initial spectra to a feedforward neural network to generate a plurality of modified theoretically generated spectra, sending an output of the feedforward neural network and empirically collected spectra to a discriminatory convolutional neural network, determining that the discriminatory convolutional neural network does not discriminate between the modified theoretically generated spectra and empirically collected spectra, and thereafter, generating a plurality of training spectra from the feedforward neural network.

The current disclosure describes techniques for managing vertical alignment or overlay in semiconductor manufacturing using machine learning. Alignments of interconnection features in a fan-out WLP process are evaluated and managed through the disclosed techniques. Big data and neural networks system are used to correlate the overlay error source factors with overlay metrology categories. The overlay error source factors include tool related overlay source factors, wafer or die related overlay source factors and processing context related overlay error source factors.