Recent progress on fabrication of memristor and transistor-based neuromorphic devices for high signal processing speed with low power consumption

Han

Adv Materials Technologies

et al. 2018

179

126

required. Several types of emerging mem ories have been researched in the past few decades such as magnetic memory, phase change memory, ferroelectric tunnel junc tions, and resistive switching memory. Among these emerging devices, resistive switching memory called memristors, introduced by Chua in 1971, [1] have strong points of small cell size, nonvolatile and random data access possibility, easy fabri cation process, and simple structure. [2,3] Because of these advantages, various mate rials are examined for achieving memris tive properties.In addition, different from the past sev eral decades, information is being made depending on experiences or repeated stimuli similar to that in the human brain. The human brain contains ≈10 11 neurons and 10 15 synapses, occupies a small space, and consumes less than 20 W, which is lower than the power required to run a household light bulb. [4][5][6] Moreover, the human brain is currently considered as the most intelligent and fastest operation system. Therefore, neuromorphic computing, which emu lates the human brain, has been regarded as a promising nextgeneration computing system. Studies on neuromorphic computing have been rapidly growing and highlighted for various applications such as artificial intelligence, sensors, robotic devices, and memory devices.Existing neural networks are implemented by the combination of machine learning as software and the von Neumann archi tecture as hardware based on the complementary metaloxide semiconductor (CMOS) technology. However, CMOSbased cir cuits require 6-12 transistors and the design is not flexible. [7] The present computing system with the von Neumann architecture is implemented by a serial operation through a central processing unit (CPU). Because of the von Neumann bottleneck, memory devices have limitations in data processing speed between memory and CPU and require high power and large space. [8][9][10] Therefore, a new neuromorphic computing system that is exe cuted by parallel operation with a high operation speed, low energy consumption, and small volume is critically required.To achieve such requirement, memristive materials have been actively examined as emulating several functions of human brain. A memristor could act as a single unit of synapse without software programming supports. Memristorbased neu romorphic architecture is implemented by parallel operation with efficient power, small volume, and high data processing Neuromorphic architectures are in the spotlight as promising candidates for substituting current computing systems owing to their high operation speed, scale-down ability, and, especially, low energy consumption. Among candidate materials, memristors have shown excellent synaptic behaviors such as spike time-dependent plasticity and spike rate-dependent plasticity by gradually changing their resistance state according to electrical input stimuli. Memristor can work as a single synapse without programming support, which remarkably satisfies the requirements of neuromorphic computing. Here, the mo...

Section: Introductionmentioning

confidence: 99%

Recent Advances in Memristive Materials for Artificial Synapses

Han

Adv Materials Technologies

et al. 2018

179

126

“…Resistance range and threshold voltages are closely related to the materials used in memristor fabrication (Hadiyawarman et al, 2018), which therefore constitute important factors for memristor-based circuit designs. In this paper, the threshold voltages and the resistance ranges are assumed to meet the needs of our design.…”

Section: Memristor Modelmentioning

confidence: 99%

Hardware Implementation of Differential Oscillatory Neural Networks Using VO 2-Based Oscillators and Memristor-Bridge Circuits

et al. 2021

Oscillatory Neural Networks (ONNs) are currently arousing interest in the research community for their potential to implement very fast, ultra-low-power computing tasks by exploiting specific emerging technologies. From the architectural point of view, ONNs are based on the synchronization of oscillatory neurons in cognitive processing, as occurs in the human brain. As emerging technologies, VO2 and memristive devices show promising potential for the efficient implementation of ONNs. Abundant literature is now becoming available pertaining to the study and building of ONNs based on VO2 devices and resistive coupling, such as memristors. One drawback of direct resistive coupling is that physical resistances cannot be negative, but from the architectural and computational perspective this would be a powerful advantage when interconnecting weights in ONNs. Here we solve the problem by proposing a hardware implementation technique based on differential oscillatory neurons for ONNs (DONNs) with VO2-based oscillators and memristor-bridge circuits. Each differential oscillatory neuron is made of a pair of VO2 oscillators operating in anti-phase. This way, the neurons provide a pair of differential output signals in opposite phase. The memristor-bridge circuit is used as an adjustable coupling function that is compatible with differential structures and capable of providing both positive and negative weights. By combining differential oscillatory neurons and memristor-bridge circuits, we propose the hardware implementation of a fully connected differential ONN (DONN) and use it as an associative memory. The standard Hebbian rule is used for training, and the weights are then mapped to the memristor-bridge circuit through a proposed mapping rule. The paper also introduces some functional and hardware specifications to evaluate the design. Evaluation is performed by circuit-level electrical simulations and shows that the retrieval accuracy of the proposed design is comparable to that of classic Hopfield Neural Networks.

“…Changes in synaptic weight between presynaptic and postsynaptic neurons are known as synaptic plasticity, including short‐term plasticity (STP), long‐term plasticity (LTP), spike‐timing‐dependent plasticity (STDP), etc. [ 9–11 ] It is confirmed that STP is related to computational functions associated with spatiotemporal information, and LTP lays the foundation for memory and learning in the neural system. [ 12 ] And STDP is the learning rule of the Hebbian theory, including asymmetric STDP, symmetric STDP, and anti‐STDP.…”

Section: Introductionmentioning

confidence: 99%

2D Material Based Synaptic Devices for Neuromorphic Computing

Cao

Peng

Chen

et al. 2020

Adv Funct Materials

184

168

The demand for computing power has been increasing exponentially since the emergence of artificial intelligence (AI), internet of things (IoT), and machine learning (ML), where novel computing primitives are required. Brain inspired neuromorphic computing systems, capable of combining analog computing and data storage at the device level, have drawn great attention recently. In addition, the basic electronic devices mimicking the biological synapse have achieved significant progress. Owing to their atomic thickness and reduced screening effect, the physical properties of 2D materials could be easily modulated by various stimuli, which is quite beneficial for synaptic applications. In this article, aiming at high‐performance and functional neuromorphic computing applications, a comprehensive review of synaptic devices based on 2D materials is provided, including the advantages of 2D materials and heterostructures, various robust multifunctional 2D synaptic devices, and associated neuromorphic applications. Challenges and strategies for the future development of 2D synaptic devices are also discussed. This review will provide an insight into the design and preparation of 2D synaptic devices and their applications in neuromorphic computing.