Tomas Tuma

Stochastic phase-change neurons

Artificial neuromorphic systems based on populations of spiking neurons are an indispensable tool in understanding the human brain and in constructing neuromimetic computational systems. To reach areal and power efficiencies comparable to those seen in biological systems, electroionics-based and phase-change-based memristive devices have been explored as nanoscale counterparts of synapses. However, progress on scalable realizations of neurons has so far been limited. Here, we show that chalcogenide-based phase-change materials can be used to create an artificial neuron in which the membrane potential is represented by the phase configuration of the nanoscale phase-change device. By exploiting the physics of reversible amorphous-to-crystal phase transitions, we show that the temporal integration of postsynaptic potentials can be achieved on a nanosecond timescale. Moreover, we show that this is inherently stochastic because of the melt-quench-induced reconfiguration of the atomic structure occurring when the neuron is reset. We demonstrate the use of these phase-change neurons, and their populations, in the detection of temporal correlations in parallel data streams and in sub-Nyquist representation of high-bandwidth signals.

High-speed multiresolution scanning probe microscopy based on Lissajous scan trajectories

A novel scan trajectory for high-speed scanning probe microscopy is presented in which the probe follows a two-dimensional Lissajous pattern. The Lissajous pattern is generated by actuating the scanner with two single-tone harmonic waveforms of constant frequency and amplitude. Owing to the extremely narrow frequency spectrum, high imaging speeds can be achieved without exciting the unwanted resonant modes of the scanner and without increasing the sensitivity of the feedback loop to the measurement noise. The trajectory also enables rapid multiresolution imaging, providing a preview of the scanned area in a fraction of the overall scan time. We present a procedure for tuning the spatial and the temporal resolution of Lissajous trajectories and show experimental results obtained on a custom-built atomic force microscope (AFM). Real-time AFM imaging with a frame rate of 1 frame s⁻¹ is demonstrated.

Dual-Stage Nanopositioning for High-Speed Scanning Probe Microscopy

This paper presents a dual-stage approach to nanopositioning in which the tradeoff between the scanner speed and range is addressed by combining a slow, large-range scanner with a short-range scanner optimized for high-speed, high-resolution positioning. We present the design, finite-element simulations, and experimental characterization of a fast custom-built short-range scanner. The short-range scanner is based on electromagnetic actuation to provide high linearity, has a clean, high-bandwidth dynamical response and is equipped with a low-noise magnetoresistance-based sensor. By using advanced noise-resilient feedback controllers, the dual-stage system allows large-range positioning with subnanometer closed-loop resolution over a wide bandwidth. Experimental results are presented in which the dual-stage scanner system is used for imaging in a custom-built atomic force microscope.

All-memristive neuromorphic computing with level-tuned neurons

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

The Four Pillars of Nanopositioning for Scanning Probe Microscopy: The Position Sensor, the Scanning Device, the Feedback Controller, and the Reference Trajectory

Since its birth in the 1980s, nanotechnology has significantly advanced our understanding and control of physical processes on the nanometer and subnanometer scale. Manipulation and interrogation of matter on these length scales have become indispensable in various fields of engineering and science and have been instrumental in some of the most exciting scientific and engineering breakthroughs of the past decades. Application areas include scanning probe microscopy (SPM) [1], data storage [2], and semiconductor device fabrication [3]. In all these applications, control of motion and position at length scales down to the size of a single atom, often referred to as nanopositioning, is a key enabling tool.

Optimal scan trajectories for high-speed scanning probe microscopy

A novel method is presented which enables the systematic analysis and design of scan trajectories for highspeed scanning probe microscopy. The analysis is based on a family of universal metrics for spatial resolution which utilize Voronoi tessellations. The scan trajectories are designed in the framework of mathematical optimization in which the specifications on spatial resolution, speed and frequency content are captured in an objective function and a set of constraints. We demonstrate the method by designing scan trajectories that are based on Lissajous curves. Experimental results are obtained on a custom-built atomic force microscope. By employing the Lissajous scan trajectories, frame rates as high as 1 frame/s are achieved using a low-bandwidth commercial nanopositioner.

Impulsive control for fast nanopositioning

In this paper we present a non-linear control scheme for high-speed nanopositioning based on impulsive control. Unlike in the case of a linear feedback controller, the controller states are altered in a discontinuous manner at specific instances in time. Using this technique, it is possible to simultaneously achieve good tracking performance, disturbance rejection and tolerance to measurement noise. Impulsive control is demonstrated experimentally on an atomic force microscope. A significant improvement in tracking performance is demonstrated.

Temporal correlation detection using computational phase-change memory

Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units. As computation becomes increasingly data centric and the scalability limits in terms of performance and power are being reached, alternative computing paradigms with collocated computation and storage are actively being sought. A fascinating such approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. We present an experimental demonstration using one million phase change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Its result is imprinted in the conductance states of the memory devices. The results of using such a computational memory for processing real-world data sets show that this co-existence of computation and storage at the nanometer scale could enable ultra-dense, low-power, and massively-parallel computing systems.New computing paradigms, such as in-memory computing, are expected to overcome the limitations of conventional computing approaches. Sebastian et al. report a large-scale demonstration of computational phase change memory (PCM) by performing high-level computational primitives using one million PCM devices.

Nanopositioning With Impulsive State Multiplication: A Hybrid Control Approach

Nanopositioning is a key enabling technology for nanoscale science and engineering. Many nanopositioning systems employ feedback control to guarantee precise and repeatable positioning. However, achieving the desired performance with conventional feedback systems has remained a challenge. This paper analyzes a novel hybrid control architecture for high-speed nanopositioning, which is based on impulsive control. By impulsively changing the states of the feedback controller, performance objectives can be met that are beyond the limitations of linear feedback. We analyze the stability and performance of impulsive feedback control, and present experimental results in which impulsive control is used for precise motion control in a high-speed scanning-probe microscope.

Detecting Correlations Using Phase-Change Neurons and Synapses

As the conventional von Neumann-based computational architectures reach their scalability and performance limits, alternative computational frameworks inspired by biological neuronal networks hold promise to revolutionize the way we process information. Here, we present a bioinspired computational primitive that utilizes an artificial spiking neuron equipped with plastic synapses to detect temporal correlations in data streams in an unsupervised manner. We demonstrate that the internal states of the neuron and of the synapses can be efficiently stored in nanoscale phase-change memory devices and show computations with collocated storage in an experimental setting.