Nanomanipulation with atomic force microscopes (AFMs) for nanoparticles with overall sizes on the order of 10 nm has been hampered in the past by the large spatial uncertainties encountered in tip positioning. This paper addresses the compensation of nonlinear effects of creep and hysteresis on the piezo scanners which drive most AFMs. Creep and hysteresis are modeled as the superposition of fundamental operators, and their inverse model is obtained by using the inversion properties of the Prandtl-Ishlinskii operator. Identification of the parameters in the forward model is achieved by a novel method that uses the topography of the sample and does not require position sensors. The identified parameters are used to compute the inverse model, which in turn serves to drive the AFM in an open-loop, feedforward scheme. Experimental results show that this approach effectively reduces the spatial uncertainties associated with creep and hysteresis, and supports automated, computer-controlled manipulation operations that otherwise would fail.Note to Practitioners-Manipulation at the nanoscale by using AFMs as sensory robots is well established in research laboratories, and has great potential as a process for prototyping nanodevices and systems, for repairing structures built by other means, and for small batch manufacturing by using multitip arrays. However, precise (to 1 nm, say) AFM nanomanipulation is currently very labor intensive, primarily because of the uncertainty in the position of the AFM tip relative to the sample being manipulated. Positional errors are due to thermal drift and various nonlinearities exhibited by the piezoelectric scanners used by most AFMs. This paper describes a technique for compensating creep and hysteresis, which, after drift, are the major causes of spatial uncertainty in AFMs. The compensator introduced here has been tested experimentally and shown to reduce creep and hysteresis effects by more than an order of magnitude. The creep and hysteresis compensator in this paper, together with the drift compensation scheme discussed in an earlier paper by the authors, provide means to reduce spatial uncertainties to a level that enables automatic manipulation, without a user in the loop, and therefore promise to greatly increase the throughput and accuracy of nanomanipulation operations.

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Interactive manipulation of nanoparticles by mechanically pushing them with the tip of an Atomic Force Microscope (AFM) is now performed routinely at many laboratories around the world. However, a human in the loop introduces significant inaccuracies and results in a very slow process, mostly because of the need to locate the particles before and after the manipulation operations in the presence of large spatial uncertainties, which are often comparable to the size of the particles. In this paper we describe the nanomanipulation systems developed at USC's Laboratory for Molecular Robotics during the last decade, culminating in a fully automatic system that is capable of accurately positioning small nanoparticles, with diameters of around 10 nm. This system uses software compensators for the non-linearities inherent in the piezoelectric actuators used in most AFMs. The planner and execution systems are described, as well as the software architecture of the systems. Experimental results are presented that validate the approach and show that nanoparticle patterns that would take hours to build interactively can now be built in minutes. Automatic operation makes it possible to use manipulation to construct much more complex nanostructures than those built in the past.