DESIGN AND CONTROL OF A SENSOR-GUIDED NANOROBOT

Miniaturization is a key step in the design of integrated circuits. Intelligent systems working on the microand nanometer scale, i. e. "nanorobots", would be of great value in this miniaturization phase. They would help optimizing the design and production process by acquiring local sample information and they even might work as nanotools to fix tiny imperfections. To create such a nanorobot we extended the capabilities of an atomic force microscope (AFM) beyond the standard surface-imaging with nanometer-resolution by adding additional controlloops for real-time signal processing. This local information control-loop enables our nanorobot to automatically search for specific features and track them. Here, we present setup and performance of our current system prototype consisting of a 2D linear motor as sample stage and an AFM-like local probe. I. I NTRODUCTION In the micro and nano world, visualization tools play a fundamental role in localizing targets and positioning miniaturized tools relative to them [1]. Traditionally, farfield optics such as light or electron microscopes are used to achieve this task. However, with such sensors alone, practical resolution and accuracy tends to be limited to several tens of nanometers. Electron microscopes may offer higher resolution but their use is restricted to vacuum environments. In contrast to far-field sensors, near-field probes typically operate in vacuum, ambient pressure, or fluid environments and offer nanometer-scale resolution, although with a much reduced field of view due to their proximity to the substrate. In the (sub)micro world, the size of objects dictates the useful dimensions of sensors and actuators and generally necessitates integration of several sensing and/or actuation functions into a single pointed probe to gain full access to the small region of interest. Such sensors and actuators suitable for the nanometer world are already available from scanning probe microscopes [2]; however, autonomous nanorobots with overall dimensions in the sub-millimeter range are not yet feasible because of the size of current microprocessor technology and interface electronics. Therefore, today’s scanning probe microscopes equipped with multifunctional sensors offer a ood platform to implement and test concepts and control strategies for nanorobots. In such a sensor-guided nanorobot, near-field probes are not primarily used in a microscope setup, i. e. to generate images, but rather as very sensitive and local tools to analyze and automatically follow user-specified nano-structures. To this end, we have developed strategies allowing the robot system to handle several different sensor inputs from a single multifunctional near-field probe [3] and to combine this information with global data and the user’s a priori knowledge. II. SETUP OF THE NANOROBOT In our current nanorobot setup [4] we have chosen AFM tips as local sensors whose multifunctional characteristics allow us to measure different signals, e. g. topography or electrical potential, as well as to use them for nano-particle handling [5, 6, 7]. This sensor is mounted on a piezo tube scanner that applies scanning probe techniques to position the tip with nanometer precision. We extended this system with a coarse positioning device to enlarge the limited working area of the piezo scanner allowing the nanorobot to work on the whole surface of integrated circuits. In detail, we achieve a working range of 200 x 200 mm with a 2D linear stepping motor used for moving the sample’s region of interest inside the piezo tube’s working space. Fig. 1 shows the basic setup which was complemented with several optical camera sensors to extend the tip’s field of view and to provide wide-range observation of the specimen. This mechanical system was further complemented by adding a controller consisting of a digital signal processor subsystem (DSP, Fulcrum DT3808, Data Translation) for local data processing and a standard personal computer (Pentium) for global data processing. The DSP subsystem is running independently of the host system under the real-time operating system SPOX allowing on-line acquisition and processing of near-field signals from the local sensor. The host system operates under Windows 95 enabling the far-field image processing as well as the data visualization for the graphical user interface to run in parallel tasks. Further, it provides a convenient interface that easily allows us to check local and global data as well as to interact with the robot system. We implemented our first prototype of a sensor-guided nanorobot on a commercial atomic force microscope (AFM, Nanoscope IIIa, Digital Instruments, Santa Barbara). To this end, interfaces were added to the AFM controller for the position signals (in x, y, z), for sensor signals and for the mode switches governing the operating functions of the near-field probe, i. e. tapping mode for topography acquisition where the nanoprobe is oscillating in z-direction and only in brief contact with the substrate during each cycle, lift mode where the nanoprobe retraces the surface at a set distance above to perform non-contact measurements like electric potential mapping [3], and approach to bring the nanoprobe into the near-field of the substrate. Care was taken to design a modular system allowing us to use the AFM as a pure microscope with the Nanoscope controller alone or as a sensor-guided nanorobot together with the external controller. III. T RACKING OF LOCAL SIGNALS We implemented a tracking algorithm [11] capable of searching for microstructures with user-specified characteristics and following them under control of a vision system or user commands. Fig. 2 illustrates the flow chart of this tracking strategy. The user specifies the region of interest and the characteristics of the structure to be located and analyzed, e. g. its approximate width b, its height and , the electrical potential, the slope and , the signal level , etc. The nanorobot then initiates a search procedure where data is acquired along extended line scans in the specified area and then compared with an analytical model of the desired structure, i. e. the shape of a circuit path and the potential over this path (Eq. 1) or an edge.