Memristive devices based on single ZnO nanowires - from material synthesis to neuromorphic functionalities

Memristive and resistive switching devices are considered promising building blocks for the realization of artificial neural networks and neuromorphic systems. Besides conventional top-down memristive devices based on thin films, resistive switching devices based on nanowires (NWs) have attracted great attention, not only for the possibility of going beyond current scaling limitations of the top-down approach, but also as model systems for the localization and investigation of the physical mechanism of switching. This work reports on the fabrication of memristive devices based on ZnO NWs, from NW synthesis to single NW-based memristive cell fabrication and characterization. The bottom-up synthesis of ZnO NWs was performed by low-pressure Chemical Vapor Deposition (LPCVD) according to a self-seeding Vapor-Solid (VS) mechanism on a Pt substrate over large scale (∼ cm2), without the requirement of previous seed deposition. The grown ZnO NWs are single crystalline with wurtzite crystal structure and are vertically aligned respect to the growth substrate. Single NWs were then contacted by means of asymmetric contacts, with an electrochemically active and an electrochemically inert electrode, to form NW-based electrochemical metallization memory (ECM) cells that show reproducible resistive switching behaviour and neuromorphic functionalities including short-term synaptic plasticity and Paired Pulse Facilitation (PPF). Besides representing building blocks for NW-based memristive and neuromorphic systems, these single crystalline devices can be exploited as model systems to study physicochemical processing underlaying memristive functionalities thanks to the high localization of switching events on the ZnO crystalline surface.

Neuromorphic Chip Integrated with an LSI and Amorphous-metal-oxide Semiconductor Thin-film Synapse Devices

Artificial intelligences are promising in future societies, and neural networks are typical technologies with the advantages such as self-organization, self-learning, parallel distributed computing, and fault tolerance, but their size and power consumption are large. Neuromorphic systems are biomimetic systems from the hardware level, with the same advantages as living brains, especially compact size, low power, and robust operation, but some well-known ones are non-optimized systems, so the above benefits are only partially gained, for example, machine learning is processed elsewhere to download fixed parameters. To solve these problems, we are researching neuromorphic systems from various viewpoints. In this study, a neuromorphic chip integrated with an LSI and amorphous-metal-oxide semiconductor (AOS) thin-film synapse devices has been developed. The neuron elements are digital circuit, which are made in an LSI, and the synapse devices are analog devices, which are made of the AOS thin film and directly integrated on the LSI. This is the world's first hybrid chip where neuron elements and synapse devices of different functional semiconductors are integrated, and local autonomous learning is utilized, which becomes possible because the AOS thin film can be deposited without heat treatment and there is no damage to the underneath layer, and has all advantages of neuromorphic systems.

Evolutionary vs. imitation learning for neuromorphic control at the edge

Neuromorphic computing offers the opportunity to implement extremely low power artificial intelligence at the edge. Control applications, such as autonomous vehicles and robotics, are also of great interest for neuromorphic systems at the edge. It is not clear, however, what the best neuromorphic training approaches are for control applications at the edge. In this work, we implement and compare the performance of evolutionary optimization and imitation learning approaches on an autonomous race car control task using an edge neuromorphic implementation. We show that the evolutionary approaches tend to achieve better performing smaller network sizes that are well-suited to edge deployment, but they also take significantly longer to train. We also describe a workflow to allow for future algorithmic comparisons for neuromorphic hardware on control applications at the edge.

BrainFreeze: Expanding the Capabilities of Neuromorphic Systems Using Mixed-Signal Superconducting Electronics

Superconducting electronics (SCE) is uniquely suited to implement neuromorphic systems. As a result, SCE has the potential to enable a new generation of neuromorphic architectures that can simultaneously provide scalability, programmability, biological fidelity, on-line learning support, efficiency and speed. Supporting all of these capabilities simultaneously has thus far proven to be difficult using existing semiconductor technologies. However, as the fields of computational neuroscience and artificial intelligence (AI) continue to advance, the need for architectures that can provide combinations of these capabilities will grow. In this paper, we will explain how superconducting electronics could be used to address this need by combining analog and digital SCE circuits to build large scale neuromorphic systems. In particular, we will show through detailed analysis that the available SCE technology is suitable for near term neuromorphic demonstrations. Furthermore, this analysis will establish that neuromorphic architectures built using SCE will have the potential to be significantly faster and more efficient than current approaches, all while supporting capabilities such as biologically suggestive neuron models and on-line learning. In the future, SCE-based neuromorphic systems could serve as experimental platforms supporting investigations that are not feasible with current approaches. Ultimately, these systems and the experiments that they support would enable the advancement of neuroscience and the development of more sophisticated AI.

Dynamic resistive switching devices for neuromorphic computing

Neuromorphic systems that can emulate the structure and the operations of biological neural circuits have long been viewed as a promising hardware solution to meet the ever-growing demands of big-data analysis and AI tasks. Recent studies on resistive switching or memristive devices have suggested such devices may form the building blocks of biorealistic neuromorphic systems. In a memristive device, the conductance is determined by a set of internal state variables, allowing the device to exhibit rich dynamics arising from the interplay between different physical processes. Not only can these devices be used for compute-in-memory architectures to tackle the von Neumann bottleneck, the switching dynamics of the devices can also be used to directly process temporal data in a biofaithful fashion. In this Review, we analyze the physical mechanisms that govern the dynamic switching behaviours and highlight how these properties can be utilized to efficiently implement synaptic and neuronal functions. Prototype systems that have been used in machine learning and brain-inspired network implementations will be covered, followed with discussions on the challenges for large scale implementations and opportunities for building bio-inspired, highly complex computing systems.

Recent Advancements in Emerging Neuromorphic Device Technologies

The explosive growth of data and information has motivated technological developments in computing systems that utilize them for efficiently discovering patterns and gaining relevant insights. Inspired by the structure and functions of biological synapses and neurons in the brain, neural network algorithms that can realize highly parallel computations have been implemented on conventional silicon transistor-based hardware. However, synapses composed of multiple transistors allow only binary information to be stored, and processing such digital states through complicated silicon neuron circuits makes low-power and low-latency computing difficult. Therefore, the attractiveness of the emerging memories and switches for synaptic and neuronal elements, respectively, in implementing neuromorphic systems, which are suitable for performing energy-efficient cognitive functions and recognition, is discussed herein. Based on a literature survey, recent progress concerning memories shows that novel strategies related to materials and device engineering to mitigate challenges are presented to primarily achieve nonvolatile analog synaptic characteristics. Attempts to emulate the role of the neuron in various ways using compact switches and volatile memories are also discussed. It is hoped that this review will help direct future interdisciplinary research on device, circuit, and architecture levels of neuromorphic systems. Corresponding author(s) Email: [email protected]