Pavlov's dog associative learning demonstrated on synaptic-like organic transistors

In this letter, we present an original demonstration of an associative learning neural network inspired by the famous Pavlov's dogs experiment. A single nanoparticle organic memory field effect transistor (NOMFET) is used to implement each synapse. We show how the physical properties of this dynamic memristive device can be used to perform low power write operations for the learning and implement short-term association using temporal coding and spike timing dependent plasticity based learning. An electronic circuit was built to validate the proposed learning scheme with packaged devices, with good reproducibility despite the complex synaptic-like dynamic of the NOMFET in pulse regime

Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity

Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of the brain operation, and second to identify a scalable microelectronic technology capable of reproducing some of the inherent functions of the human brain, such as the high synaptic connectivity (~104) and the peculiar time-dependent synaptic plasticity. Here we demonstrate unsupervised learning and tracking in a spiking neural network with memristive synapses, where synaptic weights are updated via brain-inspired spike timing dependent plasticity (STDP). The synaptic conductance is updated by the local time-dependent superposition of pre-and post-synaptic spikes within a hybrid one-transistor/one-resistor (1T1R) memristive synapse. Only 2 synaptic states, namely the low resistance state (LRS) and the high resistance state (HRS), are sufficient to learn and recognize patterns. Unsupervised learning of a static pattern and tracking of a dynamic pattern of up to 4 Ã\u97 4 pixels are demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural networks

A Contribution Towards Intelligent Autonomous Sensors Based on Perovskite Solar Cells and Ta2O5/ZnO Thin Film Transistors

Many broad applications in the field of robotics, brain-machine interfaces, cognitive computing, image and speech processing and wearables require edge devices with very constrained power and hardware requirements that are challenging to realize. This is because these applications require sub-conscious awareness and require to be always “on”, especially when integrated with a sensor node that detects an event in the environment. Present day edge intelligent devices are typically based on hybrid CMOS-memristor arrays that have been so far designed for fast switching, typically in the range of nanoseconds, low energy consumption (typically in nano-Joules), high density and endurance (exceeding 1015 cycles). On the other hand, sensory-processing systems that have the same time constants and dynamics as their input signals, are best placed to learn or extract information from them. To meet this requirement, many applications are implemented using external “delay” in the memristor, in a process which enables each synapse to be modeled as a combination of a temporal delay and a spatial weight parameter. This thesis demonstrates a synaptic thin film transistor capable of inherent logic functions as well as compute-in-memory on similar time scales as biological events. Even beyond a conventional crossbar array architecture, we have relied on new concepts in reservoir computing to demonstrate a delay system reservoir with the highest learning efficiency of 95% reported to date, in comparison to equivalent two terminal memristors, using a single device for the task of image processing. The crux of our findings relied on enhancing our capability to model the unique physics of the device, in the scope of the current thesis, that is not amenable to conventional TCAD simulations. The model provides new insight into the redox characteristics of the gate current and paves way for assessment of device performance in compute-in-memory applications. The diffusion-based mechanism of the device, effectively enables time constants that have potential in applications such as gesture recognition and detection of cardiac arrythmia. The thesis also reports a new orientation of a solution processed perovskite solar cell with an efficiency of 14.9% that is easily integrable into an intelligent sensor node. We examine the influence of the growth orientation on film morphology and solar cell efficiency. Collectively, our work aids the development of more energy-efficient, powerful edge-computing sensor systems for upcoming applications of the IOT

Multiscale modeling for application-oriented optimization of resistive random-access memory

Memristor-based neuromorphic systems have been proposed as a promising alternative to von Neumann computing architectures, which are currently challenged by the ever-increasing computational power required by modern artificial intelligence (AI) algorithms. The design and optimization of memristive devices for specific AI applications is thus of paramount importance, but still extremely complex, as many dierent physical mechanisms and their interactions have to be accounted for, which are, in many cases, not fully understood. The high complexity of the physical mechanisms involved and their partial comprehension are currently hampering the development of memristive devices and preventing their optimization. In this work, we tackle the application-oriented optimization of Resistive Random-Access Memory (RRAM) devices using a multiscale modeling platform. The considered platform includes all the involved physical mechanisms (i.e., charge transport and trapping, and ion generation, diusion, and recombination) and accounts for the 3D electric and temperature field in the device. Thanks to its multiscale nature, the modeling platform allows RRAM devices to be simulated and the microscopic physical mechanisms involved to be investigated, the device performance to be connected to the material's microscopic properties and geometries, the device electrical characteristics to be predicted, the effect of the forming conditions (i.e., temperature, compliance current, and voltage stress) on the device's performance and variability to be evaluated, the analog resistance switching to be optimized, and the device's reliability and failure causes to be investigated. The discussion of the presented simulation results provides useful insights for supporting the application-oriented optimization of RRAM technology according to specific AI applications, for the implementation of either non-volatile memories, deep neural networks, or spiking neural networks

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 characters are displayed and it is robust to a device-to-device variability of up to +/-30%

Memristors : a journey from material engineering to beyond Von-Neumann computing

Memristors are a promising building block to the next generation of computing systems. Since 2008, when the physical implementation of a memristor was first postulated, the scientific community has shown a growing interest in this emerging technology. Thus, many other memristive devices have been studied, exploring a large variety of materials and properties. Furthermore, in order to support the design of prac-tical applications, models in different abstract levels have been developed. In fact, a substantial effort has been devoted to the development of memristive based applications, which includes high-density nonvolatile memories, digital and analog circuits, as well as bio-inspired computing. In this context, this paper presents a survey, in hopes of summarizing the highlights of the literature in the last decade