Automated Micromanipulation of Micro Objects

In recent years, research efforts in the development of Micro Electro Mechanical Systems, (MEMS) including microactuators and micromanipulators, have attracted a great deal of attention. The development of microfabrication techniques has resulted in substantial progress in the miniaturization of devices such as electronic circuits. However, the research in MEMS still lags behind in terms of the development of reliable tools for post-fabrication processes and the precise and dexterous manipulation of individual micro size objects. Current micromanipulation mechanisms are prone to high costs, a large footprint, and poor dexterity and are labour intensive. To overcome such, the research in this thesis is focused on the utilization of microactuators in micromanipulation. Microactuators are compliant structures. They undergo substantial deflection during micromanipulation due to the considerable surface micro forces. Their dominance in governing micromanipulation is so compelling that their effects should be considered in designing microactuators and microsensors. In this thesis, the characterization of the surface micro forces and automated micromanipulation are investigated. An inexpensive experimental setup is proposed as a platform to replace Atomic Force Microscopy (AFM) for analyzing the force characterization of micro scale components. The relationship between the magnitudes of the surface micro forces and the parameters such as the velocity of the pushing process, relative humidity, temperature, hydrophilicity of the substrate, and surface area are empirically examined. In addition, a precision automated micromanipulation system is realized. A class of artificial neural networks (NN) is devised to estimate the unmodelled micro forces during the controlled pushing of micro size object along a desired path. Then, a nonlinear controller is developed for the controlled pushing of the micro objects to guarantee the stability of the closed loop system in the Lyapunov sense. To validate the performance of the proposed controller, an experimental setup is designed. The application of the proposed controller is extended to precisely push several micro objects, each with different characteristics in terms of the surface micro forces governing the manipulation process. The proposed adaptive controller is capable of learning to adjust its weights effectively when the surface micro forces change under varying conditions. By using the controller, a fully automated sequential positioning of three micro objects on a flat substrate is performed. The results are compared with those of the identical sequential pushing by using a conventional linear controller. The results suggest that artificial NNs are a promising tool for the design of adaptive controllers to accurately perform the automated manipulation of multiple objects in the microscopic scale for microassembly

CAD-guided automated nanoassembly using atomic force microscopy-based nonrobotics

Nanoassembly using atomic force microscopy (AFM) is a promising technique for nanomanufacturing. Most AFM-based nanoassembly schemes are implemented either manually using haptic devices or in an interactive way between the users and the atomic force microscope images. These schemes are time consuming and inefficient. Therefore, the computer-aided design (CAD)-guided automated nanoassembly using AFM is desirable for nanomanufacturing. In this paper, a general framework for CAD-guided automated nanoassembly using AFM is developed. Based on the CAD model of a nanostructure, the manipulation paths for both nanoparticles and nanorods are generated automatically. A local scanning method is developed to compensate for the random drift that may cause the failure of the nanoassembly. The experimental results demonstrate that the developed general framework can be employed to manufacture nanostructures efficiently. The research work opens a door to the CAD-guided automated nanomanufacturing using AFM. Note to Practitioners - Atomic force microscope (AFM)-based nanoassembly will lead to potential breakthroughs in manufacturing new revolutionary industrial products because many potential nanostructures and nanodevices are asymmetric, which cannot be manufactured using self-assembly only. In order to increase the efficiency and accuracy of AFM-based nanoassembly, automated computer-aided-design (CAD)-guided nanoassembly is desirable to manufacture nanostructures and nanodevices. Based on the CAD model, the environment model and the model of the nanoobjects, collision-free paths are generated to control the AFM tip to manipulate nanoobjects. A local scanning method is developed to obtain the actual position of each nanoobject to compensate for the random drift. Since the building materials of nanostructures and nanodevices may include nanoparticles, nanorods, nanowires, nanotubes, etc., automated path planning algorithms are developed for both nanoparticles and nanorods. The experimental results show that the developed general framework can be used to manufacture nanostructures more efficiently. © 2006 IEEE.Link_to_subscribed_fulltex