31 research outputs found
Redox memristors with volatile threshold switching behavior for neuromorphic computing
The spiking neural network (SNN), closely inspired by the human brain, is one of the most powerful platforms to enable highly efficient, low cost, and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system. In the hardware implementation, the building of artificial spiking neurons is fundamental for constructing the whole system. However, with the slowing down of Moore’s Law, the traditional complementary metal-oxide-semiconductor (CMOS) technology is gradually fading and is unable to meet the growing needs of neuromorphic computing. Besides, the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices. Memristors with volatile threshold switching (TS) behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems. Herein, the state-of-the-art about the fundamental knowledge of SNNs is reviewed. Moreover, we review the implementation of TS memristor-based neurons and their systems, and point out the challenges that should be further considered from devices to circuits in the system demonstrations. We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors
Reconfigurable cascaded thermal neuristors for neuromorphic computing
While the complementary metal-oxide semiconductor (CMOS) technology is the
mainstream for the hardware implementation of neural networks, we explore an
alternative route based on a new class of spiking oscillators we call thermal
neuristors, which operate and interact solely via thermal processes. Utilizing
the insulator-to-metal transition in vanadium dioxide, we demonstrate a wide
variety of reconfigurable electrical dynamics mirroring biological neurons.
Notably, inhibitory functionality is achieved just in a single oxide device,
and cascaded information flow is realized exclusively through thermal
interactions. To elucidate the underlying mechanisms of the neuristors, a
detailed theoretical model is developed, which accurately reflects the
experimental results. This study establishes the foundation for scalable and
energy-efficient thermal neural networks, fostering progress in brain-inspired
computing
Neuro-inspired electronic skin for robots
Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin–like or peripheral nervous system–like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics
Current localisation and redistribution as the basis of discontinuous current controlled negative differential resistance in NbOx
In-situ thermo-reflectance imaging is used to show that the discontinuous,
snap-back mode of current-controlled negative differential resistance (CC-NDR)
in NbOx-based devices is a direct consequence of current localization and
redistribution. Current localisation is shown to result from the creation of a
conductive filament either during electroforming or from current bifurcation
due to the super-linear temperature dependence of the film conductivity. The
snap-back response then arises from current redistribution between regions of
low and high current-density due to the rapid increase in conductivity created
within the high current density region. This redistribution is further shown to
depend on the relative resistance of the low current-density region with the
characteristics of NbOx cross-point devices transitioning between continuous
and discontinuous snap-back modes at critical values of film conductivity,
area, thickness and temperature, as predicted. These results clearly
demonstrate that snap-back is a generic response that arises from current
localization and redistribution within the oxide film rather than a
material-specific phase transition, thus resolving a long-standing controversy.Comment: 21 Page