Memristor-Based Read/Write Circuit with Stable Continuous Read Operation

Abstract

In recent years, computation-intensive applications, such as artificial intelligence, video processing and encryption, have been developing rapidly. On the other hand, the problems of “storage wall” and “power consumption wall” for the traditional storage and computing separated architectures limit the computing performance. The computational circuits and memory cells based on nonvolatile memristors are unified and become a competitive solution to this problem. However, there are various problems that prevent memristor-based circuits from entering practical applications, one of which is the memristor state deviation problem caused by continuous reading. In this paper, we study some circuits studied by predecessors on read/write circuit, compare the experimental results, analyze the reason for the resistance state deviation of memristor, and put forward a new parallel structure of memristor based on opposite polarity. The logic “1” and logic “0” are represented by the positive and negative voltage difference of two memristors with opposite polarity, which can effectively alleviate the problem of the resistance state deviation caused by continuous reading. A reading voltage of 2 V is applied to the four circuits at the same time, and continuous reading is carried out until the output voltage becomes stable. The voltage offset of the optimized circuit when reading logic “0” is reduced to 78 mV, which is significantly smaller than that of other circuits. In addition, when reading logic “1”, it has the effect of enhancing the information stored in the memristor.

Graphene-based RRAM devices for neural computing

“…By combining the storage element (memristor) and the access device (transistor) into a single unit, the overall footprint of the memory array can be reduced. There are various ways to arrange the memristors, depending on the desired application and circuit design (Lu et al, 2022 ). The two-memristor crossbar array is a grid-like structure where the two memristors are positioned at the intersection of a row and a column.…”

“…Graphene is a single layer of carbon atoms arranged in a hexagonal lattice, and it is a highly conductive and transparent material. Other 2D materials, such as transition metal dichalcogenides (TMDs) and hexagonal boron nitride (h-BN), also exhibit interesting properties that make them promising for use in neural computing (Zhang et al, 2022).…”

“…A perceptron model is experimentally in Sparvoli and Marma (2018). Lu et al (2022) have developed a two-terminal memristor synapse based on a silicon-argon composite film. In the case of the biological synapse, the weight is varied by the release of neurotransmitters from the preneuron induced by spikes.…”

Multimodal Mood Consistency and Mood Dependency Neural Network Circuit Based on Memristors

“…That being said, the memristor, proposed by Professor Cai and manufactured by HP Lab [2], has the characteristics of a small size and a low power consumption, as well as the advantages of nonlinearity and non-volatility. The realization of brain-like intelligence based on memristors can greatly promote the development of artificial neural networks for associative memory [3][4][5][6][7][8][9][10][11][12] and emotional intelligence [13][14][15][16][17][18][19]. With the rapid development of artificial intelligence, more and more people are paying attention to the research on brain-like emotional intelligence [20][21][22][23].…”