Low dimensional materials and devices for neuromorphic computing

Contemporarily, the superior performance of low-dimensional heterostructure devices has attracted extensive attention of scientists. In order to offer a clearer understanding of low-dimensional materials and heterostructure devices, this paper introduces the basic concepts of low-dimensional materials and heterostructure fabrication and arranges the devices constructed by two-dimensional heterostructure materials. Primarily, the background information of state-of-art low-dimensional materials is demonstrated. Moreover, starting from the preparation of low dimensional materials, the mainstream methods of fabricating the two-dimensional materials are discussed. Then, the fabrication methods of two-dimensional material heterostructure and the general classification of two-dimensional material heterostructure devices are summarized. Last but not least, the full text is summarized and prospected. This paper aims to provide a more specific reference and guidance for the development of two-dimensional heterostructure devices in the future.

In the tide of artificial intelligence evolution, the demand for data computing has exploded, and the von Neumann architecture computer with separate memory and computing units require cumbersome data interaction, which leads to serious degradation in performance and efficiency. Biologically inspired neuromorphic engineering performs digital/analog computations in memory, with massive parallelism and high energy efficiency, making it a promising candidate to get out of the woods. Memristive device‐based artificial synapses and neurons are building blocks to form hardware neural networks for computing acceleration. In addition, it enables the implementation of integrated bionic perception and motion systems to mimic the human peripheral nervous system for information sensing and processing. Herein, the biological basis and inspiration are described first, and the memristive synapses and circuit‐emulation neurons used for neuromorphic engineering are addressed and evaluated as well as the mechanisms. The computational acceleration and bionic perception motion integration of neuromorphic systems are discussed. Finally, the challenges and opportunities for neuromorphic engineering to accelerate computation and enrich biomimetic perception motion functions are prospected, and it is hoped that light is shed on future advances.

Three-dimensional Integrated Circuits (3D-ICs) is a cutting-edge design methodology of placing the circuitry vertically aiming for a high-speed and energy-efficient system with the smallest design area. In this paper, a novel 3D neuromorphic system is proposed and analyzed, which utilizes the fabricated two-layer memristor as the electronic synapses in a spiking neural network (SNN). The two-layer structure of the memristors leads to a significant improvement in the design area (2×), power consumption (1.48 ×), and latency (2.58×), compared to the traditional one-layer configuration. Meanwhile, the heat dissipation layers are added to our memristors reducing 30% cycle-to-cycle switching variation. Our memristive synapses are utilized for storing the exported weights of the SNNs that have threshold function as the activation function. The proposed neuromorphic system is evaluated using a hardware-software co-design approach importing the weights of SNNs into NeuroSIM. The simulation results demonstrate the significant improvement of memristive synapses on design area, power consumption, and latency, compared with the SRAM (Static Random-access Memory) and other state-of-the-art memristive synapses (10% to 66%).

Structural and electronic properties of substitutionally doped SiAs monolayer

Mimicking Pavlovian conditioning by memristive synapse is significant to implement neuromorphic computing at the hardware level. In this work, we demonstrated the Pavlovian conditioning based on the artificial synapse architecture of one-transistor/one-resistor (1T1R), which included an AgInSbTe/α-C-based memristor as a variable resistance and an N-MOS transistor. Thanks to stable resistance switching behavior of memristor and outstanding controllability of device conductance by transistor gating of 1T1R, the experimental demonstration of the acquisition and extinction of Pavlovian conditioning were realized. Moreover, the temporal relation between the conditioned and unconditioned stimuli was also established in which the memory time of associative learning decreased with the increase in the interval of two stimuli. This work provided an idea to biorealistically mimic the Pavlovian conditioning, paving the way for memristive neuromorphic computing.

All-in-one metal-oxide heterojunction artificial synapses for visual sensory and neuromorphic computing systems

The state-of-the-art artificial intelligence technologies mainly rely on deep learning algorithms based on conventional computers with classical von Neumann computing architectures, where the memory and processing units are separated resulting in an enormous amount of energy and time consumed in the data transfer process. Inspired by the human brain acting like an ultra-highly efficient biological computer, neuromorphic computing is proposed as a technology for hardware implementation of artificial intelligence. Artificial synapses are the main component of a neuromorphic computing architecture. Memristors are considered to be a relatively ideal candidate for artificial synapse applications due to their high scalability and low power consumption. Oxides are most widely used in memristors due to the ease of fabrication and high compatibility with complementary metal-oxide-semiconductor processes. However, oxide memristors suffer from unsatisfactory stability and reliability. Oxide-based hybrid structures can effectively improve the device stability and reliability, therefore providing a promising prospect for the application of oxide memristors to neuromorphic computing. This work reviews the recent advances in the development of hybrid oxide memristive synapses. The discussion is organized according to the blending schemes as well as the working mechanisms of hybrid oxide memristors.

This chapter describes the use of Raman spectroscopy and mapping analysis for the characterization of low dimensional nanostructures, including 2D sheets (graphene oxide, graphene sheets, MoS2, siloxene), and one-dimensional carbyne chains. The Raman mapping analysis and their application towards understanding the molecular level interactions in these low dimensional materials, nanostructured polymer composites, and nanopaints are also discussed. The stoichiometric composition and structure of these low dimensional materials were correlated with the Raman spectral and mapping analysis. Further, Raman spectroscopy for understanding or probing the mechanism of mechanical to electrical energy harvesting properties of carbyne films via the structural transformation from cumulene to polynne networks of carbyne is demonstrated.

2D group-IV dipnictide monolayers with intrinsic auxeticity and negative piezoelectricity are predicted to be applied as nanoscale auxetic sensors and low dimensional electromechanical devices.

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Challenges in the implementation of Nanoelectronics

Quantum state tomography and the reconstruction of the photon number distribution are techniques to extract the properties of a light field from measurements of its mean and fluctuations. These techniques are particularly useful when dealing with macroscopic or mesoscopic systems, where a description limited to the second order autocorrelation soon becomes inadequate. In particular, the emission of nonclassical light is expected from mesoscopic quantum dot systems strongly coupled to a cavity or in systems with large optical nonlinearities. We analyze the emission of a quantum dot-semiconductor optical amplifier system by quantifying the modifications of a femtosecond laser pulse propagating through the device. Using a balanced detection scheme in a self-heterodyning setup, we achieve precise measurements of the quadrature components and their fluctuations at the quantum noise limit1. We resolve the photon number distribution and the thermal-to-coherent evolution in the photon statistics of the emission. The interferometric detection achieves a high sensitivity in the few photon limit. From our data, we can also reconstruct the second order autocorrelation function with higher precision and time resolution compared with classical Hanbury Brown-Twiss experiments.

Electronic and optical properties of two-dimensional GaN/ZnO heterojunction tuned by different stacking configurations

Developing low dimensional multifunctional devices becomes more and more important as the scale of the field-effect transistors decreases. Although van der Waals (vdW) contacts between two-dimensio...

Utilization of poly (4-styrenesulfonic acid) doped polyaniline as a hole transport layer of organic solar cell for indoor applications