Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.