Nanotechnology: a systems and control approach

Recent years have seen significant advances in the field of nanosciences and nanotechnology. A significant part of research in nanotechnology deals with developing tools and devices to probe and manipulate matter at the atomic, molecular and macro-molecular levels. Surprisingly in spite of the potential for engineers to contribute substantially to this area, most of the contributions till date have come from physicists and biologists. Engineering ideas primarily from systems theory and control significantly complement the physical studies performed in this area of research. This thesis demonstrates this by the application of systems ideas and tools to address two of the most important goals of nanotechnology, interrogation and positioning of materials at the nanoscale. The atomic force microscope (AFM), a micro-cantilever based device is one of the foremost tools in the interrogation and manipulation of matter at the atomic scale. The AFM operating in the most common tapping-mode has a highly complex dynamics due to the nonlinear tip-sample interaction forces. A systems approach is proposed to analyze the tapping-mode dynamics. The systems perspective is further exploited to develop analytical tools for modeling and identifying tip sample interactions. Some of the distinctly nonlinear features of tapping-mode operation are explained using the asymptotic theory of weakly nonlinear oscillators developed by Bogoliubov and Mitropolski. In the nanopositioning front, through the design and implementation of nanopositioning devices, a new paradigm for the systematic design of nanopositioners with specific bandwidth, resolution and robustness requirements is presented. Many tools from modern robust control like nominal and robust H infinity designs and Glover McFarlane designs are exploited for this. The experimental results demonstrate the efficacy of these design schemes. There is significant improvement in performance compared to the current schemes employed in industry

Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Spiking neural networks (SNN) are artificial computational models that have been inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventional artificial neural networks, though their full computational capabilities are yet to be explored. Recently, computational memory architectures based on non-volatile memory crossbar arrays have shown great promise to implement parallel computations in artificial and spiking neural networks. In this work, we experimentally demonstrate for the first time, the feasibility to realize high-performance event-driven in-situ supervised learning systems using nanoscale and stochastic phase-change synapses. Our SNN is trained to recognize audio signals of alphabets encoded using spikes in the time domain and to generate spike trains at precise time instances to represent the pixel intensities of their corresponding images. Moreover, with a statistical model capturing the experimental behavior of the devices, we investigate architectural and systems-level solutions for improving the training and inference performance of our computational memory-based system. Combining the computational potential of supervised SNNs with the parallel compute power of computational memory, the work paves the way for next-generation of efficient brain-inspired systems

Monatomic phase change memory

Phase change memory has been developed into a mature technology capable of storing information in a fast and non-volatile way, with potential for neuromorphic computing applications. However, its future impact in electronics depends crucially on how the materials at the core of this technology adapt to the requirements arising from continued scaling towards higher device densities. A common strategy to finetune the properties of phase change memory materials, reaching reasonable thermal stability in optical data storage, relies on mixing precise amounts of different dopants, resulting often in quaternary or even more complicated compounds. Here we show how the simplest material imaginable, a single element (in this case, antimony), can become a valid alternative when confined in extremely small volumes. This compositional simplification eliminates problems related to unwanted deviations from the optimized stoichiometry in the switching volume, which become increasingly pressing when devices are aggressively miniaturized. Removing compositional optimization issues may allow one to capitalize on nanosize effects in information storage