This article presents the design, identification, and control of a nano-positioning device suited to image biological samples as part of an atomic force microscope. The device is actuated by a piezoelectric stack and its motion is sensed by a linear variable differential transformer. It is demonstrated that the conventional proportional-integral control architecture does not meet the bandwidth requirements for positioning. The design and implementation of an H∞ controller demonstrates substantial improvements in the positioning speed and precision, while eliminating the undesirable nonlinear effects of the actuator. The characterization of the resulting device in terms of bandwidth, resolution, and repeatability provided illustrates the effectiveness of the modern robust control paradigm.

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Three factors are currently driving the main developments in artificial intelligence (AI): availability of vast amounts of data, continuous growth in computing power, and algorithmic innovations. Graphics processing units (GPUs) have been demonstrated as effective co-processors for the implementation of machine learning (ML) algorithms based on deep learning (DL). Solutions based on DL and GPU implementations have led to massive improvements in many AI tasks, but have also caused an exponential increase in demand for computing power. Recent analyses show that the demand for computing power has increased by a factor of 300 000 since 2012, and the estimate is that this demand will double every 3.4 months-at a much faster rate than improvements made historically through Moore's scaling (a sevenfold improvement over the same period of time). [1] At the same time, Moore's law has been slowing down significantly for the last few years, [2] as there are strong indications that we will not be able to continue scaling down complementary metal-oxide-semiconductor (CMOS) transistors. This calls for the exploration of alternative technology roadmaps for the development of scalable and efficient AI solutions. Transistor scaling is not the only way to improve computing performance. Architectural innovations, such as GPUs, field-programmable arrays (FPGAs), and application-specific integrated circuits (ASICs), have all significantly advanced the ML field. [3] A common aspect of modern computing architectures for ML is a move away from the classical von-Neumann architecture that physically separates memory and computing. This approach yields a performance bottleneck that is often the main reason for both energy and speed inefficiency of ML implementations on conventional hardware platforms due to

The atomic force microscope(AFM) is a powerful tool for investigating surfaces at atomic scales. Harmonic balance and power balance techniques are introduced to analyze the tapping-mode dynamics of the atomic force microscope. The harmonic balance perspective explains observations hitherto unexplained in the AFM literature. A nonconservative model for the cantilever-sample interaction is developed. The energy dissipation in the sample is studied and the resulting power balance equations combined with the harmonic balance equations are used to estimate the model parameters. Experimental results confirm that the harmonic and power balance tools can be used effectively to predict the behavior of the tapping cantilever. Keywords Atomic force microscopes, Surface dynamics Disciplines Electrical and Computer Engineering CommentsThe following article appeared in Journal of Applied Physics 89, 6473 (2001) The atomic force microscope ͑AFM͒ is a powerful tool for investigating surfaces at atomic scales. Harmonic balance and power balance techniques are introduced to analyze the tapping-mode dynamics of the atomic force microscope. The harmonic balance perspective explains observations hitherto unexplained in the AFM literature. A nonconservative model for the cantilever-sample interaction is developed. The energy dissipation in the sample is studied and the resulting power balance equations combined with the harmonic balance equations are used to estimate the model parameters. Experimental results confirm that the harmonic and power balance tools can be used effectively to predict the behavior of the tapping cantilever.

In typical dynamic mode operation of atomic force microscopes, steady state signals like amplitude and phase are used for detection and imaging of material. In these methods, the resolution and bandwidth are dictated by the quality factor (Q) of the cantilever. In this letter, we present a methodology that exploits the deflection signal during the transient of the cantilever motion. The principle overcomes the fundamental limitations on the trade off between resolution and bandwidth present in existing methods and makes it independent of the quality factor. Experimental results provided corroborate the theoretical development.