International Business Machines Corporation

Nam H Nguyen

Jayant R Kalagnanam

Ramachandran Muralidhar

Young Min Lee

Robert J Baseman

Christian Makaya

Kyong Min Yeo

Edward John Pring

Hui Qi

Dung Tien Phan

Dzung Tien Phan

Fateh Ali Tipu

Dhaval Patel

Anuradha Bhamidipaty

Wesley M Gifford

Dhavalkumar C Patel

Fateh A Tipu

Lam Nguyen

Bhanukiran Vinzamuri

A method for process control using predictive long short term memory includes obtaining historical post-process measurements taken on prior products of the manufacturing process; obtaining historical in-process measurements taken on prior workpieces during the manufacturing process; training a neural network to predict each of the historical post-process measurements, in response to the corresponding historical in-process measurements and preceding historical post-process measurements; obtaining present in-process measurements on a present workpiece during the manufacturing process; predicting a future post-process measurement for the present workpiece, by providing the present in-process measurements and the historical post-process measurements as inputs to the neural network; and adjusting at least one controllable variable of the manufacturing process in response to the prediction of the future post-process measurement.

Tool control using multistage LSTM for predicting on-wafer measurements

In an approach for compressing a neural network, a processor receives a neural network, wherein the neural network has been trained on a set of training data. A processor receives a compression ratio. A processor compresses the neural network based on the compression ratio using an optimization model to solve for sparse weights. A processor re-trains the compressed neural network with the sparse weights. A processor outputs the re-trained neural network.

Compression of deep neural networks

A method of improving at least one of quality and yield of a physical process comprises: obtaining values, from respective performances of the physical process, for a plurality of variables associated with the physical process; determining at least one Gaussian mixture model (GMM) representing the values for the variables for the performances of the physical process; based at least in part on the at least one GMM, computing at least one anomaly score for at least one of the variables for at least one of the performances of the physical process; based on the at least one anomaly score, identifying the at least one of the performances of the physical process as an outlier; and, based at least in part on the outlier identification, modifying the at least one of the variables for one or more subsequent performances of the physical process.

Model management for non-stationary systems

A computer implemented method of administering a complex system includes receiving multivariate data from a plurality of sensors of the system in an ambient state. Event sequences in the received multivariate data are identified. The multivariate event sequences are projected to a lower stochastic latent embedding. A temporal structure of the sequences is learned in a lower latent space. A probabilistic prediction in the lower latent space is provided. The probabilistic prediction in the lower stochastic latent space is decoded to an event prediction in the ambient state.

Event time characterization and prediction in multivariate event sequence domains to support improved process reliability

Anomaly detection and remedial recommendation techniques for improving the quality and yield of microelectronic products are provided. In one aspect, a method for quality and yield improvement via anomaly detection includes: collecting time series sensor data during individual steps of a semiconductor manufacturing process; calculating anomaly scores for each of the individual steps using a predictive model; and implementing changes to the semiconductor manufacturing process based on the anomaly scores. A system for quality and yield improvement via anomaly detection is also provided.

Anomaly detection and remedial recommendation

Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Orchestration of learning and execution of model predictive control tool for manufacturing processes

Methods and systems for determining an optimized set point for a manufacturing apparatus are described. In an example, a processor may receive observed data from the manufacturing apparatus. The observed data may include data collected by the manufacturing apparatus based on at least one resolution. The processor may generate feature data based on the observed data. The processor may determine a first model and a second model based on the feature data. The first model may relate to a first prediction of a key performance indicator of the manufacturing apparatus in a first amount of future time. The second model may relate to a second prediction of the key performance indicator of the manufacturing apparatus in a second amount of future time. The processor may determine the optimized set point based on an objective relating to the first model and based on a constraint relating to the second model.

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Pleasantville, NY, United States of America

Nam H Nguyen