Xerox Corporation

Palo Alto Research Center Inc.

Sergey Butylkov

Eugene Michael Chow

Anne Plochowietz

Lara S Crawford

Bradley Rupp

Matthew Adam Shreve

Jengping Lu

Sourobh Raychaudhuri

Julie A Bert

Warren Bruce Jackson

Jeng Ping Lu

Anand Ramakrishnan

Disclosed are methods and systems of controlling the placement of micro-objects on the surface of a micro-assembler. Control patterns may be used to cause electrodes of the micro-assembler to generate dielectrophoretic (DEP) and electrophoretic (EP) forces which may be used to manipulate, move, position, or orient one or more micro-objects on the surface of the micro-assembler. The control patterns may be part of a library of control patterns.

Method of controlling the placement of micro-objects on a micro-assembler

Control loop latency can be accounted for in predicting positions of micro-objects being moved by using a hybrid model that includes both at least one physics-based model and machine-learning models. The models are combined using gradient boosting, with a model created during at least one of the stages being fitted based on residuals calculated during a previous stage based on comparison to training data. The loss function for each stage is selected based on the model being created. The hybrid model is evaluated with data extrapolated and interpolated from the training data to prevent overfitting and ensure the hybrid model has sufficient predictive ability. By including both physics-based and machine-learning models, the hybrid model can account for both deterministic and stochastic components involved in the movement of the micro-objects, thus increasing the accuracy and throughput of the micro-assembly.

System and method for machine-learning-based position estimation for use in micro-assembly control with the aid of a digital computer

Apparatus for controlling the placement of micro-objects on a micro-assembler

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Oakland, CA, United States of America

Sergey Butylkov