Application of Memristors in Microwave Passive Circuits

The recent implementation of the fourth fundamental electric circuit element, the memristor, opened new vistas in many fields of engineering applications. In this paper, we explore several RF/microwave passive circuits that might benefit from the memristor salient characteristics. We consider a power divider, coupled resonator bandpass filters, and a low-reflection quasi-Gaussian lowpass filter with lossy elements. We utilize memristors as configurable linear resistors and we propose memristor-based bandpass filters that feature suppression of parasitic frequency pass bands and widening of the desired rejection band. The simulations are performed in the time domain, using LTspice, and the RF/microwave circuits under consideration are modeled by ideal elements available in LTspice

Toward bio-inspired information processing with networks of nano-scale switching elements

Unconventional computing explores multi-scale platforms connecting molecular-scale devices into networks for the development of scalable neuromorphic architectures, often based on new materials and components with new functionalities. We review some work investigating the functionalities of locally connected networks of different types of switching elements as computational substrates. In particular, we discuss reservoir computing with networks of nonlinear nanoscale components. In usual neuromorphic paradigms, the network synaptic weights are adjusted as a result of a training/learning process. In reservoir computing, the non-linear network acts as a dynamical system mixing and spreading the input signals over a large state space, and only a readout layer is trained. We illustrate the most important concepts with a few examples, featuring memristor networks with time-dependent and history dependent resistances

Quantum Memristors in Quantum Photonics

We propose a method to build quantum memristors in quantum photonic platforms. We firstly design an effective beam splitter, which is tunable in real-time, by means of a Mach-Zehnder-type array with two equal 50:50 beam splitters and a tunable retarder, which allows us to control its reflectivity. Then, we show that this tunable beam splitter, when equipped with weak measurements and classical feedback, behaves as a quantum memristor. Indeed, in order to prove its quantumness, we show how to codify quantum information in the coherent beams. Moreover, we estimate the memory capability of the quantum memristor. Finally, we show the feasibility of the proposed setup in integrated quantum photonics

Simulating Memristive Networks in SystemC-AMS

This chapter presents a solution for the simulation of large memristive networks with SystemC-AMS. SystemC-AMS allows simulating memristors both on analogue level and on digital level to link analogue memristive devices to digital circuits and system level specifications. We investigate the benefits and drawbacks of a SystemC-AMS simulation compared to a simulation in SPICE. We show for the example of a two-layer memristive network emulating an optical flow algorithm by the detection of moving edges that large memristive networks can be simulated with a free available SystemC-AMS simulation environment, whereas free available SPICE simulation environment fails. However, it is also shown that commercial SPICE simulators are superior against current SystemC-AMS implementations concerning the size of simulated memristive networks. However, SystemC-AMS simulations of memristive networks offer both still more flexibility and similar run times compared to commercial SPICE simulators for small-sized memristive networks. The flexibility and the powerfulness of a SystemC-AMS solution is demonstrated for a complex network that solves edge detection, filtering and detecting of moving objects. The possible run times of the memristive network are determined in the SystemC-AMS simulation environment and are compared with an optical flow algorithm on classical hardware like a CPU and a GPU

A Multi-Value 3D Crossbar Array Nonvolatile Memory Based on Pure Memristors

© 2022, The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1140/epjs/s11734-022-00576-9How to improve the storage density and solve the sneak path current problem has become the key to the design of nonvolatile memristive memory. In this paper, a high storage density and high reading/writing speed 3D crossbar array non-volatile memory based on pure memristors is proposed. The main works are as follows: (1) an extensible memristive cluster is proposed, (2) a memristive switch is designed, and (3) a 3D crossbar array non-volatile memory is constructed. The memory cell of the 3D crossbar array non-volatile memory is constructed by pure memristors and can be extended by adding memristor in a memristive cluster or adding memristive clusters in a memory cell to realize multi-value storage. The memristive switch can effectively reduce the sneak path current effect. The pure memristive memory cell solves the conflict between the storage density and sneak path current effect and greatly improves the storage density of memory cells. Furthermore, the 3D cross-array structure allows different memory cells on the same layer or different layers to be read and written in parallel, which greatly improves the speed of reading and writing. Simulations with PSpice verifies that the proposed memristive cluster can realize stable multi-value storage, has higher storage density, faster reading and writing speed, fewer input ports and output ports, better stability, and lower power consumption. Moreover, the structure proposed in this paper can also be used in the circuit design of the neuromorphic network, logic circuit, and other memristive circuits.Peer reviewe