In an embodiment, a method during execution of a motion plan by a robotic arm includes determining a voice command from speech of a user said during the execution of the motion plan, determining a modification of the motion plan based on the voice command from the speech of the user, and executing the modification of the motion plan by the robotic arm.

One or more processors generate a feature set describing evolution of a state space of a manufacturing system from time series data of sensors measuring values of control parameters and exogenous parameters of the manufacturing system, and measuring values of feature parameters of components produced by the manufacturing system. The one or more processors also generate from the feature set predicted values of at least one of the feature parameters, and alter at least one of the control parameters according to the feature set and the predicted values to drive the predicted values toward a target value or target values.

In an embodiment, a method and system use various sensors to determine a shape of a collection of materials (e.g., foodstuffs). A controller can determine a trajectory which achieves the desired end-state, possibly chosen from a set of feasible, collision-free trajectories to execute, and a robot executes that trajectory. The robot, executing that trajectory, scoops, grabs, or otherwise acquires the desired amount of material from the collection of materials at a desired location. The robot then deposits the collected material in the desired receptacle at a specific location and orientation.

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.

A method for training a neural network to derive, from an image of a camera mounted on a robot, a movement vector for the robot to insert an object into an insertion. The method includes controlling the robot to hold the object, bringing the robot into a target position in which the object is inserted in the insertion, for a plurality of positions different from the target position controlling the robot to move away from the target position to the position, taking a camera image by the camera and labelling the camera image by a movement vector to move back from the position to the target position and training the neural network using the labelled camera images.

A method for training a neural network to derive, from an image of a camera mounted on a robot, a movement vector to insert an object into an insertion. The method includes, for a plurality of positions in which the object held by the robot touches a plane in which the insertion is located controlling the robot to move to the position, taking a camera image by the camera and labelling the camera image with a movement vector between the position and the insertion in the plane and training the neural network using the labelled camera images.

A method including receiving, by a processing device, a first selection of at least one of a first fabrication process or first manufacturing equipment to perform manufacturing operations of the first fabrication process. The method can further include inputting the first selection into a digital replica of the first manufacturing equipment wherein the digital replica outputs physical conditions of the first fabrication process. The method may further include determining environmental resource usage data indicative of a first environmental resource consumption of the first fabrication process run on the first manufacturing equipment based on the physical conditions of the first fabrication process. The processing device may further determine a modification to the first fabrication process that reduces the environmental resource consumption of the first fabrication process run on the first manufacturing equipment. The method can further include performing at least one of applying the modification to the first fabrication.

A kinematics model-free trajectory tracking method for a robotic arm includes the following steps. Obtain an actual trajectory equation r.sub.a(t) of the robotic arm at time t according to a sensor, and combines the actual trajectory equation r.sub.a(t) with a predetermined target trajectory equation r.sub.d(t) to obtain a first error function e(t). Obtain a differential equation (I) of a state change rate of a driver of the robotic arm. Obtain a second error function ϵ(t). Pass the second error function c(t) through the applied gradient neural network to obtain equation (IV). Jointly solve equation (I) and equation (IV) to obtain an joint state vector θ(t) of the robotic arm. Drive a motion of the robotic arm by a controller according to the joint state vector θ(t) of the robotic arm to complete trajectory tracking.

A machine learning device includes: a measured data acquisition unit that acquires a measured data group; a thermal displacement acquisition unit that acquires a thermal displacement actual measured value about a machine element; a storage unit that uses the measured data group acquired by the measured data acquisition unit as input data, uses the thermal displacement actual measured value about the machine element acquired by the thermal displacement acquisition unit as a label, and stores the input data and the label in association with each other as teaching data; and a calculation formula learning unit that performs machine learning based on the measured data group and the thermal displacement actual measured value about the machine element, thereby setting a thermal displacement estimation calculation formula used for calculating the thermal displacement of the machine element based on the measured data group.

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.