**1. Introduction**

Memristive electronic systems, similar to biological synapses in neural networks, are a new type of electronic logic switches and memory with extremely low energy consumption and footprint. These new electronic components can solve the problem of physical and technological limitations of modern CMOS technology and create an elemental base for artificial intelligence. The unique electronic and optical properties of newly discovered atomic twodimensional (2D) crystals, such as graphene, graphene oxide, molybdenum disulfide, and so

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

on demonstrate a huge potential for designing ultrahigh density nano- and bioelectronics for innovative information systems.

the on and off states. The first matrix of memristors was made on the basis of TiO<sup>2</sup>

multilevel recording using discrete or continuous states.

bullet in order to knock it down.

requires about 106

CMOS chip in the HP laboratory in 2012. A memristor with two platinum electrodes was a nonlinear dynamic structure whose resistance depended on the electric field and the current flow (**Figure 1**). This nonlinear device made it possible to form nonvolatile states that allow storing information with the power supply off, had the ability to obtain ultrahigh recording density, low switching energy, high operating speed, long storage time and the possibility of

The memristor is a memory resistor with variable resistance and is described by the conductivity depending on the flux and field. In 2016, Fujitsu Semiconductor and Panasonic Semiconductor demonstrated the first serial product of 4 Mb RRAM. Using a nonlinear dynamic approach allows you to effectively solve a number of complex computational problems for image processing and pattern recognition. For example, a commercial product Toshiba Smart Photo Sensor with a universal chip based on a cellular neural network (CNN) is capable of processing images, similar to the human brain, which allows to calculate the elementary problems of image recognition within nanoseconds. It was shown that the CNN chip is so fast that it can detect a bullet in flight and have enough time to program another

Memristors, which are similar to synapses in biological neural networks, can become an elemental base for creating high-performance intelligent machines and computers with a neuromorphic architecture similar to the brain. It is known that the human brain, containing 1010 neurons and 10<sup>14</sup> synapses (**Figure 2**), processes analog information and consumes only about 20 Watts. A modern supercomputer with digital processing of information to simulate the operation of a neural network of only 1% of the number of neurons of the human brain

puter "K Computer" (up to 10 petaflops, 1016 billion operations per second, 1 petabyte of RAM)—the development of the Japanese corporation "Fujitsu"—takes about 40 min. Thus, an analog processor based on a neuromorphic memory system is much more efficient than a modern digital supercomputer. The key moment of this system is special processes of signal transmission in neural networks, which are paid great attention to by researchers. In 2000, the Nobel Prize in Physiology was awarded to Arvid Karlsson, Paul Gringard and Erik R. Kandell for "discoveries in the transmission of signals in the nervous system." Neural networks have

**Figure 1.** Memristors on CMOS chip (HP 2012) and the I/V-characteristic of Pt/TiO2

Watts. To simulate the work of the human brain within 1 s, the supercom-

/Pt memristor.

on a

69

Memristive Systems Based on Two-Dimensional Materials

http://dx.doi.org/10.5772/intechopen.78973

The chapter consists of the Introduction (Paragraph 1) and five sections that describe a brief history of the memristor and nonlinear effects in semiconductor electronics (Paragraph 2), the discovery of 2D crystals and a multilevel ultrafast nonvolatile memory based on graphene oxide (Paragraph 3), a memristor with a floating photogate (Paragraphs 3 and 4), a photonic chip with a photon synapse (Paragraph 5), a 2D TMD memory obtained on large-scale substrates (6) and Conclusion (Paragraph 7). Here we present a modern state of memristive systems, where signaling is analogous to signaling in biological neural networks. The focus is on 2D nonvolatile resistive memory based on molybdenum and graphene/graphene oxide (G/GO), which is biocompatible and allows the use of a neuromorphic architecture for analog computation and self-assembly technology. Photocatalytic and electron-beam oxidationreduction of graphene/graphene oxide is considered as an effective method of manufacturing 2D memristors with photoresistive switching for nonvolatile memory of ultrahigh capacity. A new type of multifunctional memristor with a photogate, controlled electrically and optically over a wide range of wavelengths, can be used for image processing, pattern recognition and recognition of sounds, movements and speech needed to create artificial intelligence.

### **2. Memristor and nonlinear effects in solid state electronics**

The definition of the memristor as a nonlinear resistive element was introduced by Leon Chua in 1971 to describe the missing fourth base element of the electrical circuit [1]. The memristor, along with other known circuit elements, such as a capacitor, a resistor and an inductor, could describe nonlinear effects in solid state electronics that were already well known. In 1922, Oleg Losev observed a new phenomenon of negative differential resistance in a two-electrode point device—a cristadyne [2, 3]—which was then used to generate and detect a signal for radio broadcasting around the world. Losev's cristadyne allowed to work at frequencies up to 100 MHz, at that time not conceivable and not understandable for applications. Later, Oleg Losev improved his cristadyne, adding to it a third electrode, which could control the current in this device. The article on the new nonlinear three-electrode device, sent by Losev to the "Physical Review" in 1942 from besieged Leningrad was lost and not published. The great interest in this topic was also in other laboratories. In 1948, John Bardeen of Bell Labs received a patent for a point-contact three-electrode element [4] and, together with Walter Brattein, described the physical principles of the transistor effect [5–9]. In 1956, for the discovery of the transistor effect, William Shockley, John Bardeen and Walter Brattein received the Nobel Prize in Physics. In 1957, Leo Esaki demonstrated independently a similar nonlinear device—a tunnel diode—and in 1973 received for the discovery of this effect the Nobel Prize in Physics.

Interest in the nonlinear two-electrode memristive device increased sharply in 2008, when the memristor was detected experimentally in the HP laboratory [10]. This device consisted of two nanoscale regions, doped and undoped, the relative displacement of which controlled the on and off states. The first matrix of memristors was made on the basis of TiO<sup>2</sup> on a CMOS chip in the HP laboratory in 2012. A memristor with two platinum electrodes was a nonlinear dynamic structure whose resistance depended on the electric field and the current flow (**Figure 1**). This nonlinear device made it possible to form nonvolatile states that allow storing information with the power supply off, had the ability to obtain ultrahigh recording density, low switching energy, high operating speed, long storage time and the possibility of multilevel recording using discrete or continuous states.

on demonstrate a huge potential for designing ultrahigh density nano- and bioelectronics for

The chapter consists of the Introduction (Paragraph 1) and five sections that describe a brief history of the memristor and nonlinear effects in semiconductor electronics (Paragraph 2), the discovery of 2D crystals and a multilevel ultrafast nonvolatile memory based on graphene oxide (Paragraph 3), a memristor with a floating photogate (Paragraphs 3 and 4), a photonic chip with a photon synapse (Paragraph 5), a 2D TMD memory obtained on large-scale substrates (6) and Conclusion (Paragraph 7). Here we present a modern state of memristive systems, where signaling is analogous to signaling in biological neural networks. The focus is on 2D nonvolatile resistive memory based on molybdenum and graphene/graphene oxide (G/GO), which is biocompatible and allows the use of a neuromorphic architecture for analog computation and self-assembly technology. Photocatalytic and electron-beam oxidationreduction of graphene/graphene oxide is considered as an effective method of manufacturing 2D memristors with photoresistive switching for nonvolatile memory of ultrahigh capacity. A new type of multifunctional memristor with a photogate, controlled electrically and optically over a wide range of wavelengths, can be used for image processing, pattern recognition and

recognition of sounds, movements and speech needed to create artificial intelligence.

The definition of the memristor as a nonlinear resistive element was introduced by Leon Chua in 1971 to describe the missing fourth base element of the electrical circuit [1]. The memristor, along with other known circuit elements, such as a capacitor, a resistor and an inductor, could describe nonlinear effects in solid state electronics that were already well known. In 1922, Oleg Losev observed a new phenomenon of negative differential resistance in a two-electrode point device—a cristadyne [2, 3]—which was then used to generate and detect a signal for radio broadcasting around the world. Losev's cristadyne allowed to work at frequencies up to 100 MHz, at that time not conceivable and not understandable for applications. Later, Oleg Losev improved his cristadyne, adding to it a third electrode, which could control the current in this device. The article on the new nonlinear three-electrode device, sent by Losev to the "Physical Review" in 1942 from besieged Leningrad was lost and not published. The great interest in this topic was also in other laboratories. In 1948, John Bardeen of Bell Labs received a patent for a point-contact three-electrode element [4] and, together with Walter Brattein, described the physical principles of the transistor effect [5–9]. In 1956, for the discovery of the transistor effect, William Shockley, John Bardeen and Walter Brattein received the Nobel Prize in Physics. In 1957, Leo Esaki demonstrated independently a similar nonlinear device—a tunnel diode—and in 1973 received for the discovery of this

Interest in the nonlinear two-electrode memristive device increased sharply in 2008, when the memristor was detected experimentally in the HP laboratory [10]. This device consisted of two nanoscale regions, doped and undoped, the relative displacement of which controlled

**2. Memristor and nonlinear effects in solid state electronics**

innovative information systems.

68 Advances in Memristor Neural Networks – Modeling and Applications

effect the Nobel Prize in Physics.

The memristor is a memory resistor with variable resistance and is described by the conductivity depending on the flux and field. In 2016, Fujitsu Semiconductor and Panasonic Semiconductor demonstrated the first serial product of 4 Mb RRAM. Using a nonlinear dynamic approach allows you to effectively solve a number of complex computational problems for image processing and pattern recognition. For example, a commercial product Toshiba Smart Photo Sensor with a universal chip based on a cellular neural network (CNN) is capable of processing images, similar to the human brain, which allows to calculate the elementary problems of image recognition within nanoseconds. It was shown that the CNN chip is so fast that it can detect a bullet in flight and have enough time to program another bullet in order to knock it down.

Memristors, which are similar to synapses in biological neural networks, can become an elemental base for creating high-performance intelligent machines and computers with a neuromorphic architecture similar to the brain. It is known that the human brain, containing 1010 neurons and 10<sup>14</sup> synapses (**Figure 2**), processes analog information and consumes only about 20 Watts. A modern supercomputer with digital processing of information to simulate the operation of a neural network of only 1% of the number of neurons of the human brain requires about 106 Watts. To simulate the work of the human brain within 1 s, the supercomputer "K Computer" (up to 10 petaflops, 1016 billion operations per second, 1 petabyte of RAM)—the development of the Japanese corporation "Fujitsu"—takes about 40 min. Thus, an analog processor based on a neuromorphic memory system is much more efficient than a modern digital supercomputer. The key moment of this system is special processes of signal transmission in neural networks, which are paid great attention to by researchers. In 2000, the Nobel Prize in Physiology was awarded to Arvid Karlsson, Paul Gringard and Erik R. Kandell for "discoveries in the transmission of signals in the nervous system." Neural networks have

**Figure 1.** Memristors on CMOS chip (HP 2012) and the I/V-characteristic of Pt/TiO2 /Pt memristor.

**Figure 2.** Neural network.

associative memory and the ability to learn deeply, the knowledge of which was laid down in the works of the Russian physiologist Ivan Pavlov, who received the Nobel Prize in Physiology in 1904. The study of digestion pushed him to the idea of conditioned reflexes. Such acquired reflexes arise under certain conditions and disappear when conditions are not observed.
