Abstract: System and method that allow utilize machine learning algorithms to move a micro-object to a desired position are described. A sensor such as a high speed camera or capacitive sensing, tracks the locations of the objects. A dynamic potential energy landscape for manipulating objects is generated by controlling each of the electrodes in an array of electrodes. One or more computing devices are used to: estimate an initial position of a micro-object using the sensor; generate a continuous representation of a dynamic model for movement of the micro-object due to electrode potentials generated by at least some of the electrodes and use automatic differentiation and Gauss quadrature rules on the dynamic model to derive optimum potentials to be generated by the electrodes to move the micro-object to the desired position; and map the calculated optimized electrode potentials to the array to activate the electrodes.

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: The system and method described allow for real-time control over positioning of a micro-object. A movement of at least one micro-object suspended in a medium can be induced by a generation of one or more forces by electrodes proximate to the micro-object. Prior to inducing the movement, a simulation is used to develop a model describing a parameter of an interaction between each of the electrodes and the micro-object. A function describing the forces generated by an electrode and an extent of the movement induced due to the forces is generated using the model. The function is used to design closed loop policy control scheme for moving the micro-object towards a desired position. The position of the micro-object is tracked and taken into account when generating voltage patterns in the scheme.

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: The system and method described allow for real-time control over positioning of a micro-object. A movement of at least one micro-object suspended in a medium can be induced by a generation of one or more forces by electrodes proximate to the micro-object. Prior to inducing the movement, a simulation is used to develop a model describing a parameter of an interaction between each of the electrodes and the micro-object. A function describing the forces generated by an electrode and an extent of the movement induced due to the forces is generated using the model. The function is used to design closed loop policy control scheme for moving the micro-object towards a desired position. The position of the micro-object is tracked and taken into account when generating voltage patterns in the scheme.

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: One embodiment of the present disclosure provides a system and method for facilitating a search for a hybrid-manufacturing process plan for manufacturing an object. During operation, the system can obtain a set of partial order constraints constraining the order in which a set of at least two manufacturing actions, corresponding to addition or removal of predefined regions of space, appear in a process plan. The system can constrain, based on the set of partial order constraints, a search space. The search space can correspond to a tree in which the nodes represent the object's state and the edges represent available actions at each node. The system can then determine a set of optimized process plans represented by orderings of the actions, corresponding to paths on the search tree, that produce the desired final state in a cost-effective manner.

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: One embodiment of the present disclosure provides a system and method for facilitating a search for a hybrid-manufacturing process plan for manufacturing an object. During operation, the system can obtain a set of partial order constraints constraining the order in which a set of at least two manufacturing actions, corresponding to addition or removal of predefined regions of space, appear in a process plan. The system can constrain, based on the set of partial order constraints, a search space. The search space can correspond to a tree in which the nodes represent the object's state and the edges represent available actions at each node. The system can then determine a set of optimized process plans represented by orderings of the actions, corresponding to paths on the search tree, that produce the desired final state in a cost-effective manner.

Abstract: System and method that allow utilize machine learning algorithms to move a micro-object to a desired position are described. A sensor such as a high speed camera or capacitive sensing, tracks the locations of the objects. A dynamic potential energy landscape for manipulating objects is generated by controlling each of the electrodes in an array of electrodes. One or more computing devices are used to: estimate an initial position of a micro-object using the sensor; generate a continuous representation of a dynamic model for movement of the micro-object due to electrode potentials generated by at least some of the electrodes and use automatic differentiation and Gauss quadrature rules on the dynamic model to derive optimum potentials to be generated by the electrodes to move the micro-object to the desired position; and map the calculated optimized electrode potentials to the array to activate the electrodes.

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: The system and method described allow for real-time control over positioning of a micro-object. A movement of at least one micro-object suspended in a medium can be induced by a generation of one or more forces by electrodes proximate to the micro-object. Prior to inducing the movement, a simulation is used to develop a model describing a parameter of an interaction between each of the electrodes and the micro-object. A function describing the forces generated by an electrode and an extent of the movement induced due to the forces is generated using the model. The function is used to design closed loop policy control scheme for moving the micro-object towards a desired position. The position of the micro-object is tracked and taken into account when generating voltage patterns in the scheme.

Abstract: The system and method described allow for real-time control over positioning of a micro-object. A movement of at least one micro-object suspended in a medium can be induced by a generation of one or more forces by electrodes proximate to the micro-object. Prior to inducing the movement, a simulation is used to develop a model describing a parameter of an interaction between each of the electrodes and the micro-object. A function describing the forces generated by an electrode and an extent of the movement induced due to the forces is generated using the model. The function is used to design closed loop policy control scheme for moving the micro-object towards a desired position. The position of the micro-object is tracked and taken into account when generating control signals in the scheme.

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: The system and method described allow for real-time control over positioning of a micro-object. A movement of at least one micro-object suspended in a medium can be induced by a generation of one or more forces by electrodes proximate to the micro-object. Prior to inducing the movement, a simulation is used to develop a model describing a parameter of an interaction between each of the electrodes and the micro-object. A function describing the forces generated by an electrode and an extent of the movement induced due to the forces is generated using the model. The function is used to design closed loop policy control scheme for moving the micro-object towards a desired position. The position of the micro-object is tracked and taken into account when generating control signals in the scheme.

Abstract: The system and method described below allow for real-time control over positioning of a micro-object. A movement of at least one micro-object suspended in a medium can be induced by a generation of one or more forces by electrodes proximate to the micro-object. Prior to inducing the movement, a simulation is used to develop a model describing a parameter of an interaction between each of the electrodes and the micro-object. A function describing the forces generated by an electrode and an extent of the movement induced due to the forces is generated using the model. The function is used to design closed loop policy control scheme for moving the micro-object towards a desired position. The position of the micro-object is tracked and taken into account when generating control signals in the scheme.

Abstract: Embodiments of the present invention provide a system for modeling Quality of Service (QoS) including a customer's perceived QoS, and optimizing QoS and perceived QoS. The system improves significantly over previous systems and can conserve resources by optimizing the level of resource allocations to satisfy a Service Level Agreement (SLA), while satisfying the customer's impression of QoS. During operation, the system obtains an SLA between a provider and a customer, specifying a QoS metric and a corresponding range. The system then optimizes the customer's perceived QoS, which further comprises modeling the customer's perceived QoS and the QoS metric, based on an operational allocation of the provider. Optimizing the perceived QoS may further comprise determining an optimized level of the allocations that enhances the customer's modeled perceived QoS, and also corresponds to a modeled QoS metric within the range. The system may then set the provider's allocation to the optimized level.