Resistive communications based on neuristors

Memristors are passive elements that allow us to store information using a single element per bit. However, this is not the only utility of the memristor. Considering the physical chemical structure of the element used, the memristor can function at the same time as memory and as a communication unit. This paper presents a new approach to the use of the memristor and develops the concept of resistive communication

A caloritronics-based Mott neuristor

Machine learning imitates the basic features of biological neural networks to efficiently perform tasks such as pattern recognition. This has been mostly achieved at a software level, and a strong effort is currently being made to mimic neurons and synapses with hardware components, an approach known as neuromorphic computing. CMOS-based circuits have been used for this purpose, but they are non-scalable, limiting the device density and motivating the search for neuromorphic materials. While recent advances in resistive switching have provided a path to emulate synapses at the 10 nm scale, a scalable neuron analogue is yet to be found. Here, we show how heat transfer can be utilized to mimic neuron functionalities in Mott nanodevices. We use the Joule heating created by current spikes to trigger the insulator-to-metal transition in a biased VO2 nanogap. We show that thermal dynamics allow the implementation of the basic neuron functionalities: activity, leaky integrate-and-fire, volatility and rate coding. By using local temperature as the internal variable, we avoid the need of external capacitors, which reduces neuristor size by several orders of magnitude. This approach could enable neuromorphic hardware to take full advantage of the rapid advances in memristive synapses, allowing for much denser and complex neural networks. More generally, we show that heat dissipation is not always an undesirable effect: it can perform computing tasks if properly engineered

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

A walk on the frontier of energy electronics with power ultra-wide bandgap oxides and ultra-thin neuromorphic 2D materials

Altres ajuts: the ICN2 is funded also by the CERCA programme / Generalitat de CatalunyaUltra-wide bandgap (UWBG) semiconductors and ultra-thin two-dimensional materials (2D) are at the very frontier of the electronics for energy management or energy electronics. A new generation of UWBG semiconductors will open new territories for higher power rated power electronics and deeper ultraviolet optoelectronics. Gallium oxide - GaO(4.5-4.9 eV), has recently emerged as a suitable platform for extending the limits which are set by conventional (-3 eV) WBG e.g. SiC and GaN and transparent conductive oxides (TCO) e.g. In2O3, ZnO, SnO2. Besides, GaO, the first efficient oxide semiconductor for energy electronics, is opening the door to many more semiconductor oxides (indeed, the largest family of UWBGs) to be investigated. Among these new power electronic materials, ZnGa2O4 (-5 eV) enables bipolar energy electronics, based on a spinel chemistry, for the first time. In the lower power rating end, power consumption also is also a main issue for modern computers and supercomputers. With the predicted end of the Moores law, the memory wall and the heat wall, new electronics materials and new computing paradigms are required to balance the big data (information) and energy requirements, just as the human brain does. Atomically thin 2D-materials, and the rich associated material systems (e.g. graphene (metal), MoS2 (semiconductor) and h-BN (insulator)), have also attracted a lot of attention recently for beyond-silicon neuromorphic computing with record ultra-low power consumption. Thus, energy nanoelectronics based on UWBG and 2D materials are simultaneously extending the current frontiers of electronics and addressing the issue of electricity consumption, a central theme in the actions against climate chang

Quantum materials for energy-efficient neuromorphic computing

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This paper discusses select examples of these approaches, and provides a perspective for the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems

An epitaxial perovskite as a compact neuristor:Electrical self-oscillations in TbMnO₃thin films

Developing materials that can lead to compact versions of artificial neurons (neuristors) and synapses (memristors) is the main aspiration of the nascent neuromorphic materials research field. Oscillating circuits are interesting as neuristors, as they emulate the firing of action potentials. Here we present room-temperature self-oscillating devices fabricated from epitaxial thin films of semiconducting TbMnO3. We show that the negative differential resistance regime observed in these devices, orginates from transitions across the electronic band gap of the semiconductor. The intrinsic nature of the mechanism governing the oscillations gives rise to a high degree of control and repeatability. Obtaining such properties in an epitaxial perovskite oxide opens the way towards combining self-oscillating properties with those of other piezoelectric, ferroelectric, or magnetic perovskite oxides in order to achieve hybrid neuristor-memristor functionality in compact heterostructures

False Gods and Libertarians: Artificial Intelligence and Community in Amad `Abd al-Salām al-Baqqāli’s The Blue Flood and Heinlein’s The Moon is a Harsh Mistress

This paper examines Moroccan author Baqqāli\u27s novel al-Ṭūfān al-\u27Azraq [The Blue Flood, 1976] from the perspective of its use of an artificial intelligence (AI) as a guiding force in a sequestered community. In the novel, the desert refuge for scientists is controlled by a massive computer. The protagonist becomes aware that the AI has become sentient and is planning to use nuclear weapons to destroy humanity. The analysis will compare The Blue Flood to Robert A. Heinlein\u27s 1966 classic The Moon is a Harsh Mistress, wherein the AI leads the human colonists of Luna in a successful struggle for independence from Earth. In The Blue Flood a sentient being with superhuman powers can only be conceived as a form of blasphemy. From this, we can take the text as a warning to intellectuals in real-world Morocco not to dismiss Islamic and cultural traditions simply because they seem irrational. The insights gleaned from The Blue Flood open up The Moon is a Harsh Mistress to a reading that contrasts with prevailing scholarly judgment—i.e., Heinlein\u27s novel can now be read as less a failed advocacy of libertarianism than an extended critique of the unlikelihood and vulnerabilities of a libertarian society

In-memory computing with emerging memory devices: Status and outlook

Supporting data for "In-memory computing with emerging memory devices: status and outlook", submitted to APL Machine Learning