P. Stoliar, Schneegans, O., and Rozenberg, M. J., “Biologically Relevant Dynamical Behaviors Realized in an Ultra-Compact Neuron Model”, Frontiers in Neuroscience, vol. 14, p. 421, 2020. WebsiteAbstract
We demonstrate a variety of biologically relevant dynamical behaviors building on a recently introduced ultra-compact neuron (UCN) model. We provide the detailed circuits which all share a common basic block that realizes the leaky-integrate-and-fire (LIF) spiking behavior. All circuits have a small number of active components and the basic block has only three, two transistors and a silicon controlled rectifier (SCR). We also demonstrate that numerical simulations can faithfully represent the variety of spiking behavior and can be used for further exploration of dynamical behaviors. Taking Izhikevich’s set of biologically relevant behaviors as a reference, our work demonstrates that a circuit of a LIF neuron model can be used as a basis to implement a large variety of relevant spiking patterns. These behaviors may be useful to construct neural networks that can capture complex brain dynamics or may also be useful for artificial intelligence applications. Our UCN model can therefore be considered the electronic circuit counterpart of Izhikevich’s (2003) mathematical neuron model, sharing its two seemingly contradicting features, extreme simplicity and rich dynamical behavior.
J. del Valle, et al., “Subthreshold firing in Mott nanodevices”, Nature, vol. 569, p. 388-392, 2019. WebsiteAbstract
Resistive switching, a phenomenon in which the resistance of a device can be modified by applying an electric field1-5, is at the core of emerging technologies such as neuromorphic computing and resistive memories6-9. Among the different types of resistive switching, threshold firing10-14 is one of the most promising, as it may enable the implementation of artificial spiking neurons7,13,14. Threshold firing is observed in Mott insulators featuring an insulator-to-metal transition15,16, which can be triggered by applying an external voltage: the material becomes conducting ('fires') if a threshold voltage is exceeded7,10-12. The dynamics of this induced transition have been thoroughly studied, and its underlying mechanism and characteristic time are well documented10,12,17,18. By contrast, there is little knowledge regarding the opposite transition: the process by which the system returns to the insulating state after the voltage is removed. Here we show that Mott nanodevices retain a memory of previous resistive switching events long after the insulating resistance has recovered. We demonstrate that, although the device returns to its insulating state within 50 to 150?nanoseconds, it is possible to re-trigger the insulator-to-metal transition by using subthreshold voltages for a much longer time (up to several milliseconds). We find that the intrinsic metastability of first-order phase transitions is the origin of this phenomenon, and so it is potentially present in all Mott systems. This effect constitutes a new type of volatile memory in Mott-based devices, with potential applications in resistive memories, solid-state frequency discriminators and neuromorphic circuits.
J. del Valle, Gabriel Ramirez, J., Rozenberg, M. J., and Schuller, I. K., “Challenges in materials and devices for resistive-switching-based neuromorphic computing”, JOURNAL OF APPLIED PHYSICS, vol. 124, p. 211101, 2018.Abstract
This tutorial describes challenges and possible avenues for the implementation of the components of a solid-state system, which emulates a biological brain. The tutorial is devoted mostly to a charge-based (i.e. electric controlled) implementation using transition metal oxide materials, which exhibit unique properties that emulate key functionalities needed for this application. In Sec. I, we compare the main differences between a conventional computational machine, based on the Turing-von Neumann paradigm, and a neuromorphic machine, which tries to emulate important functionalities of a biological brain. We also describe the main electrical properties of biological systems, which would be useful to implement in a charge-based system. In Sec. II, we describe the main components of a possible solid-state implementation. In Sec. III, we describe a variety of Resistive Switching phenomena, which may serve as the functional basis for the implementation of key devices for neuromorphic computing. In Sec. IV, we describe why transition metal oxides are promising materials for future neuromorphic machines. Theoretical models describing different resistive switching mechanisms are discussed in Sec. V, while existing implementations are described in Sec. VI. Section VII presents applications to practical problems. We list in Sec. VIII important basic research challenges and open issues. We discuss issues related to specific implementations, novel materials, devices, and phenomena. The development of reliable, fault tolerant, energy efficient devices, their scaling, and integration into a neuromorphic computer may bring us closer to the development of a machine that rivals the brain. Published by AIP Publishing.
V. S. Nguyen, et al., “Direct Evidence of Lithium Ion Migration in Resistive Switching of Lithium Cobalt Oxide Nanobatteries”, SMALL, vol. 14, p. 1801038, 2018.Abstract
Lithium cobalt oxide nanobatteries offer exciting prospects in the field of nonvolatile memories and neuromorphic circuits. However, the precise underlying resistive switching (RS) mechanism remains a matter of debate in two-terminal cells. Herein, intriguing results, obtained by secondary ion mass spectroscopy (SIMS) 3D imaging, clearly demonstrate that the RS mechanism corresponds to lithium migration toward the outside of the LixCoO2 layer. These observations are very well correlated with the observed insulator-to-metal transition of the oxide. Besides, smaller device area experimentally yields much faster switching kinetics, which is qualitatively well accounted for by a simple numerical simulation. Write/erase endurance is also highly improved with downscaling - much further than the present cycling life of usual lithium-ion batteries. Hence very attractive possibilities can be envisaged for this class of materials in nanoelectronics.
J. del Valle, et al., “Resistive asymmetry due to spatial confinement in first-order phase transitions”, PHYSICAL REVIEW B, vol. 98, p. 045123, 2018.Abstract
We report an asymmetry in the R vs T characteristics across the first-order metal-insulator transition (MIT) of V2O3 nanowires. The resistance changes in a few, large jumps during cooling through the MIT, while it does it in a smoother way during warming. The asymmetry is greatly enhanced as the width of the nanowire approaches a characteristic domain size. Our results, together with previous reports on VO2 {[}W. Fan et al., Phys. Rev. B 83, 235102 (2011)] and FeRh {[}V. Uhlir et al., Nat. Commun. 7, 13113 (2016)] imply that asymmetry is a generic feature of first-order phase transitions in one-dimensional systems. We show that this behavior is a simple, elegant consequence of the combined effects of the transition hysteresis and the temperature dependence of the insulating gap in this case (and generically, the order parameter relevant for the physical observable). We conclude that our proposed asymmetry mechanism is universally applicable to many electronic first-order phase transitions.