An exemplary scheduler system and method are disclosed that can schedule a plurality of heterogenous robots to perform a set of tasks using heterogeneous graph attention network models. The exemplary scheduler system and method can outperform other work in multi-robot scheduling both in terms of schedule optimality and the total number of feasible schedules found and also in a scalable framework that can be trained via imitation-based Q-learning operations. The exemplary scheduler system and method can autonomously learn scheduling policies on multiple application domains.

One or more first parameters associated with an electronic device manufacturing process are monitored. An artificial neural network associated with the one or more first parameters is determined. One or more second parameters are determined using the artificial neural network. The one or more first parameters are adjusted using the one or more second parameters.

A machine learning device that performs machine learning of a welding condition for manufacturing an additively-manufactured object by welding a filler metal and depositing weld beads, the machine learning device includes: at least one hardware processor configured to perform a learning process for generating a learned model using a welding condition of a weld bead and a block pattern formed by the weld bead as input data and shape data of the weld bead as output data.

A system for controlling a tool includes a tool operable to perform tasks. A control for the tool includes processing circuitry for using machine learning to improve operation of the tool, and having access to a memory with stored data. The processing circuitry is operable to communicate with a user interface, and the user interface is operable to provide a prompt for a desired action to the control. The control is operable to break the received prompt into a plurality of sub-steps, communicate with the stored data, and make a determination as to whether the control is competent to perform each of the sub-steps. The control is operable to control the tool to perform one of the sub-steps if it has determined it is competent and to communicate to other information if it determines it is not competent to perform any others of the sub-steps. A method is also disclosed.

A selection of manufacturing equipment associated with a current fabrication process of a manufacturing environment is received. A respective eco-efficiency model corresponding to at least one of the selected manufacturing equipment is identified from a set of eco-efficiency models. Each of the set of eco-efficiency models represents a prior environmental resource consumption of a prior fabrication process involving a respective manufacturing equipment component. Values for one or more process parameters for the current fabrication process that will reduce environmental resource consumption of the current fabrication process when run using the selected manufacturing equipment are determined based on the identified respective eco-efficiency model. The determined values for the one or more process parameters are applied to the current fabrication process.

A neural network-based error compensation method for mass transfer of mini-light-emitting diode (Mini-LED) chips includes the following steps. (S1) An automated optical re-inspection result is obtained as a first result. (S2) The first result is sorted and normalized to obtain a second result. (S3) Nearest-neighbor interpolation is performed on a Mini-LED chip with a transfer status identifier being abnormal, and a differential of path variables of each Mini-LED chip is calculated. (S4) A multi-layer neural network model is defined. A loss function is constructed. A weight of the multi-layer neural network model is updated with the loss function, until no overfitting is observed. (S5) A chip transfer path is generated and input into the multi-layer neural network model to obtain a predicted transfer error value, and mass transfer of the Mini-LED chip is performed based on the predicted transfer error value.

Various embodiments relate to a method for analyzing manufacturing process data. The method includes: receiving, by a processor, a sequence of sensor outputs from a plurality of sensors monitoring a manufacturing process; predicting, using a transformer model executed by the processor, future manufacturing process parameters based on the sensor outputs; generating one or more key influencers on a current system state based on an attention matrix of the transformer model; analyzing the predicted parameters to identify an out-of-specification parameter; and identifying one or more key contributors to the out-of-specification parameter based on the attention matrix of a transformer head associated therewith.

Provided herein are methods of treating a patient afflicted with a tumor according to the tumor's microenvironments (TME). Also provided are gene panels that can be used for identifying a human subject afflicted with a cancer suitable for treatment with a particular therapeutic agent based on the subject's TME.

A dynamic blockchain-based trustworthy scheduling method and device for industrial wireless networks are provided. The method comprises: Construct an optimization model for scheduling the industrial wireless network with the goal of maximizing the trustworthy processing efficiency of tasks. performing model reconstruction on the optimization model based on a preset multi-agent Markov decision process model to obtain a target optimization model; and optimizing the target optimization model using a preset rotating multi-agent deep reinforcement learning algorithm model on the basis of observation information of industrial devices in the industrial wireless network collected in real time, to obtain target parameters corresponding to the parameters to be optimized for scheduling the industrial wireless network. The method of the present application improves the trustworthy processing efficiency of tasks in the industrial wireless network.

A characteristic prediction method includes acquiring a trained model defining an association between a serial number and a characteristic, the serial number being based on a time-series arrangement of first processes executed by a processing apparatus and an arrangement order of wafers, the arrangement order of the wafers being determined in the processing apparatus, the processing apparatus arranging the wafers and simultaneously executing the first process, the characteristic being measured in each of the wafers after a second process is executed on the wafers on which the first process has been executed, and inputting, into the trained model, first serial numbers and measured first characteristics corresponding to the first serial numbers to predict second characteristics corresponding to second serial numbers after the first serial numbers. The trained model includes a time-series model using the serial number as a time series.