WITHDRAWN (NO SHOW) EQ.11.11.07 Design and Modeling of Rare-Earth Nickelate Spiking Neurons for Neuromorphic Computing

Memristors, resistive memory devices, have gained popularity in numerous applications, from nonvolatile memory to artificial neural networks. They present an opportunity for massive energy savings, as well as improvements in physical density. That density is commonly achieved with crossbar arrays of two-terminal memristors. An abundance of research has demonstrated synaptic behavior from transistors and memristors, but neuronal behavior (i.e. spiking) remains underexplored. Recent work has demonstrated artificial neurons based on locally active memristors (operating in the negative differential resistance region) using VO₂, which has a Mott insulator-metal transition (IMT) at 340 K. The IMT of a memristor material is ideally above room temperature so that the circuit does not require cooling and unselected devices in an array are insensitive to noise, but alternative materials such as NbO₂possess too high of an IMT, 1040 K, resulting in slower switching speeds and higher energy use.<br/>Rare-earth nickelates (RNOs) are perovskite oxides in which a rare-earth ion sits on the A site. Apart from LaNiO₃, RNOs exhibit a charge-transfer IMT at temperatures spanning 100-600 K. The transition temperature varies with rare-earth cation and strain, among other things, making RNOs an intriguing class of materials for spiking neuronal behavior. In our work, we use epitaxial thin-film growth to stabilize RNO films and, along with rare-earth cation alloying, to control the IMT transition temperature.<br/>In addition, we aim to demonstrate on-chip spiking, without external RC elements, using the capacitance of the insulating active layer that surrounds the selected device, rather than an external capacitor. We use a physics-based model that relates the Joule heating of a device to the thermodynamics of its IMT to determine the amount of metallic material in an active device at a given moment.The size of that metallic channel controls the device’s conductivity. Using LT-SPICE as a non-linear solver for these governing equations, we model a single memristoras part of an oscillator circuit. As an initial model, we focus on a Pearson-Anson oscillator circuit topology because it is commonly used to describe a biological neuron ion channel.<br/>Our modeled devices consist of LaNiO₃ grown on an insulating crystalline substrate and etched into read lines, then an active layer of RNO alloy, and metallic write lines. The experimental RNO layers are grown as single-orientation thin films by physical vapor deposition, and the properties of preliminary growths are used in the SPICE modeling. For example, we measure an average sheet resistance of our LaNiO₃films of 4e-4 Ω-cm and can use this value to design the resistive component of the RC oscillator. In general, we find that the design space is limited by the higher line resistance of LaNiO₃contacts and lower capacitance of the RNO active layer, compared to previous studies using metallic contacts and external components in the oscillator circuit. However, with careful geometric design all on-chip spiking is achievable in this system.<br/>In conclusion, we describe the first round co-design of active memristors for biomimetic neurons fabricated from RNOs. First, we take measured parameters of test films to model the proposed oscillator circuit. Using physical governing equations, we construct a SPICE model of a single oscillator representing a single on-chip spiking device. Future rounds of design will consider the capabilities of nanoimprint lithography, which is very sensitive to feature size and fill factor, when determining geometric parameters such as crossbar widths, to ultimately produce an array of devices displaying biological spiking behavior.