Themis Prodromakis

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%.

Implementation of a spike-based perceptron learning rule using TiO2−x memristors

Synaptic plasticity plays a crucial role in allowing neural networks to learn and adapt to various input environments. Neuromorphic systems need to implement plastic synapses to obtain basic “cognitive” capabilities such as learning. One promising and scalable approach for implementing neuromorphic synapses is to use nano-scale memristors as synaptic elements. In this paper we propose a hybrid CMOS-memristor system comprising CMOS neurons interconnected through TiO2−x memristors, and spike-based learning circuits that modulate the conductance of the memristive synapse elements according to a spike-based Perceptron plasticity rule. We highlight a number of advantages for using this spike-based plasticity rule as compared to other forms of spike timing dependent plasticity (STDP) rules. We provide experimental proof-of-concept results with two silicon neurons connected through a memristive synapse that show how the CMOS plasticity circuits can induce stable changes in memristor conductances, giving rise to increased synaptic strength after a potentiation episode and to decreased strength after a depression episode.

A Cell Classifier for RRAM Process Development

Devices that exhibit resistive switching are promising components for future nanoelectronics with applications ranging from emerging memory to neuromorphic computing and biosensors. In this brief, we present an algorithm for identifying switchable devices, i.e., devices that can be programmed in distinct resistive states and that change their state predictably and repeatedly in response to input stimuli. The method is based on extrapolating the statistical significance of difference in between two distinct resistive states as measured from devices subjected to standardized bias protocols. The test routine is applied on distinct elements of 32×32 resistive-random-access-memory (RRAM) crossbar arrays and yields a measure of device switchability in the form of a statistical significance p-value. Ranking devices by p-value shows that switchable devices are typically found in the bottom 10% and are therefore easily distinguishable from nonfunctional devices. Implementation of this algorithm dramatically cuts RRAM testing time by granting fast access to the best devices in each array, as well as yield metrics.

Towards a memristor-based spike-sorting platform

We present a new approach for performing spike-sorting through a memristor-based, neural-signal processing platform. We have previously shown that the inherent threshold property of the memristor allows spike-detection through nonvolatile resistive state transition. Here, a test memristive device is subjected to a neural recording and the periodically recorded resistive state changes are mapped to the amplitude of the spiking events. It is found that the resistive state changes can be differentiated into clusters, where each cluster can be mapped to a range of spiking events in the input neural waveform, thus indicating the address of source neuron.

X-Ray spectromicroscopy investigation of soft and hard breakdown in RRAM devices

Resistive random access memory (RRAM) is considered an attractive candidate for next generation memory devices due to its competitive scalability, low-power operation and high switching speed. The technology however, still faces several challenges that overall prohibit its industrial translation, such as low yields, large switching variability and ultimately hard breakdown due to long-term operation or high-voltage biasing. The latter issue is of particular interest, because it ultimately leads to device failure. In this work, we have investigated the physicochemical changes that occur within RRAM devices as a consequence of soft and hard breakdown by combining full-field transmission x-ray microscopy with soft x-ray spectroscopic analysis performed on lamella samples. The high lateral resolution of this technique (down to 25 nm) allows the investigation of localized nanometric areas underneath permanent damage of the metal top electrode. Results show that devices after hard breakdown present discontinuity in the active layer, Pt inclusions and the formation of crystalline phases such as rutile, which indicates that the temperature increased locally up to 1000 K.

Effect of patterned polyacrylamide hydrogel on morphology and orientation of cultured NRVMs

We recently demonstrated that patterned Parylene C films could be effectively used as a mask for directly copolymerizing proteins on polyacrylamide hydrogel (PAm). In this work, we have proved the applicability of this technique for studying the effect such platforms render on neonatal rat ventricular myocytes (NRVMs). Firstly, we have characterised topographically and mechanically the scaffolds in liquid at the nano-scale level. We thus establish that such platforms have physical properties that closely mimics the in vivo extracellular environment of cells. We have then studied the cell morphology and physiology by comparing cultures on flat uniformly-covered and collagen-patterned scaffolds. We show that micro-patterns promote the elongation of cells along the principal axis of the ridges coated with collagen. In several cases, cells also tend to create bridges across the grooves. We have finally studied cell contraction, monitoring Ca2+ cycling at a certain stimulation. Cells seeded on patterned scaffolds present significant responses in comparison to the isotropic ones.

Conduction mechanisms at distinct resistive levels of Pt/TiO 2-x /Pt memristors

Resistive random access memories (RRAMs) are considered as key enabling components for a variety of emerging applications due to their capacity to support multiple resistive states. Deciphering the underlying mechanisms that support resistive switching remains to date a topic of debate, particularly for metal-oxide technologies, and is very much needed for optimizing their performance. This work aims to identify the dominant conduction mechanisms during switching operation of Pt/TiO2-x/Pt stacks, which is without a doubt one of the most celebrated ones. A number of identical devices were accordingly electroformed for acquiring distinct resistive levels through a pulsing-based and compliance-free protocol. For each obtained level, the switching current-voltage (I-V) characteristics were recorded and analyzed in the temperature range of 300 K–350 K. This allowed the extraction of the corresponding signature plots revealing the dominant transport mechanism for each of the I-V branches. Gradual (analogue) switching was obtained for all cases, and two major regimes were identified. For the higher resistance regime, the transport at both the high and low resistive states was found to be interface controlled due to Schottky emission. As the resistance of devices reduces to lower levels, the dominant conduction changes from an interface to the core-material controlled mechanism. This study overall supports that engineering the metal-oxide/metal electrode interface can lead to tailored barrier modifications for controlling the switching characteristics of TiO2 RRAM.Resistive random access memories (RRAMs) are considered as key enabling components for a variety of emerging applications due to their capacity to support multiple resistive states. Deciphering the underlying mechanisms that support resistive switching remains to date a topic of debate, particularly for metal-oxide technologies, and is very much needed for optimizing their performance. This work aims to identify the dominant conduction mechanisms during switching operation of Pt/TiO2-x/Pt stacks, which is without a doubt one of the most celebrated ones. A number of identical devices were accordingly electroformed for acquiring distinct resistive levels through a pulsing-based and compliance-free protocol. For each obtained level, the switching current-voltage (I-V) characteristics were recorded and analyzed in the temperature range of 300 K–350 K. This allowed the extraction of the corresponding signature plots revealing the dominant transport mechanism for each of the I-V branches. Gradual (analogue) swi...

Live demonstration: MNET: A visually rich memristor crossbar simulator

A flexible, versatile, and visually rich memristor crossbar simulator is presented in this paper. The system is represented by a Python graphical user interface (GUI) and memristor simulator engine which can instantiate crossbars of any size made out of any available memristor model. This system serves as an education tool, allowing the user to experiment with memristors in a crossbar configuration.

EU COST action IC1401 — Pushing the frontiers of memristive devices to systems

European Union COST Actions provide the opportunity for researchers who are geographically dispersed to work together towards an ambitious, multidisciplinary goal, whilst learning from each other and avoiding effort duplication. This paper gives a brief overview of work done by, but not limited to, members of EU COST Action IC1401. The presented work summary is organized around the four workgroups that tackle devices, circuit theory, circuit implementations, bioinspired and sensory systems.

A planar micro-magnetic platform for stimulation of neural cells in vitro

In this work we present a novel platform of micro-inductors designed to perform magnetic stimulation in neural cells and neural slices in vitro. The fabrication along with design considerations are presented. The fabricated platform consists of a 6×6 array of micro-inductors and achieves spatial magnetic flux profiles with a resolution of 100μm. The capability of the platform to perform magnetic stimulation is explored through finite element method calculations and by testing with a phantom gel, which mimics the electric properties of neural tissue. Biasing the micro-coils with a current of 25mA produces a magnetic flux density with a maximum of 4.28mT above their centre, while the induced electric potential reaches the 233mV in the vicinity of the coils.