US 11,921,488 B2 · System and method for machine-learning-enabled micro-object density distribution control with the aid of a digital computer

Abstract

System and method that to shape micro-object density distribution (how densely the micro-objects are assembled in particular spatial regions) are provided. A high speed camera tracks existing object density distribution. An array of photo-transistor-controlled electrodes is used to generate a dynamic potential energy landscape for manipulating objects with both DEP and EP forces, and a video projector is used actuate the array. One or more computing devices are used to: process images captured by the camera to estimate existing density distribution of objects; receive a desired density distribution of micro-objects; define a model describing a variation of micro-object density over time due to capacitance-based interactions; generate a sequence of electrode potential that when generated would minimize error between the existing density distribution and a desired density distribution; and use the sequences of electrode potentials to actuate the electrodes.

Claims20

Dependent claims

A system for micro-object density distribution control with the aid of a digital computer, comprising:

one or more processors configured to execute computer-executable code, the processors configured to:
obtain one or more parameters of a system for positioning a plurality of micro-objects, the system comprising a plurality of programmable electrodes, the electrodes configured to induce a movement of the micro-objects when the micro-objects are proximate to the electrodes upon a generation of one or more electric potentials by one or more of the electrodes;
define a model describing a variation of micro-object density over time due to capacitance-based interactions based on the parameters of the system;
estimate a density distribution of the micro-objects using at least one sensor;
receive a target density distribution of the micro-objects;
perform model predictive control (MPC) optimization to derive based on the density model a sequence of the electrode potentials to be generated by at least some of the electrodes for moving at least some of the micro-objects to minimize an error between the estimated density distribution and the target density distribution;
actuate at least some of the electrodes to generate the sequence of the electrode potentials.

4 more elements

A method for micro-object density distribution control with the aid of a digital computer, comprising:

obtaining, by one or more processors configured to execute computer-executable code, one or more parameters of a system for positioning a plurality of micro-objects, the system comprising a plurality of programmable electrodes, the electrodes configured to induce a movement of the micro-objects when the micro-objects are proximate to the electrodes upon a generation of one or more electric potentials by one or more of the electrodes;
defining by one or more of the processors, a model describing a variation of micro-object density over time due to capacitance-based interactions based on the parameters of the system;
estimating by one or more of the processors a density distribution of the micro-objects using at least one sensor;
receiving by one or more of the processors a target density distribution of the micro-objects;
performing by one or more of the processors model predictive control (MPC) optimization to derive based on the density model a sequence of the electrode potentials to be generated by at least some of the electrodes for moving at least some of the micro-objects to minimize an error between the estimated density distribution and the target density distribution;
actuating by one or more of the processors at least some of the electrodes to generate the sequence of the electrode potentials.

3 more elements

Dependencies are inferred from the claim text.

System and method for machine-learning-enabled micro-object density distribution control with the aid of a digital computer

Life of this patent

System and method for machine-learning-enabled micro-object density distribution control with the aid of a digital computer

Life of this patent

Abstract

Claims20