Phase-change materials, also well known as the Chalcogenide alloy, have received considerable attention during last two decades owing to its widespread applications in the field of the electrical storage market such as phase-change random access memory and phase-change probe memory. In addition to the storage devices, its unique electrical properties that can be dynamically tunable with respect to the electrical excitations lead to numerous novel applications represented by memristor and memristor-based neuromorphic electrical circuits. These emerging applications undoubtedly allows for a further exploitation of the potential of phase-change materials, and thus makes it advantageous over other storage medium like ferroelectric and magnetic materials. In order to help researchers understand the role of phase-change materials in these novel applications as well as their importance for citizen’s daily life, a comprehensive review that not only covers the traditional storage applications, but also the applications on these exotic devices becomes imperative. In this review, the chemical structure of phase-change materials and their remarkable electrical properties are first reviewed, followed by an introduction of their applications on the storage fields. The physical principles of various emerging electrical devices using phase-change materials are subsequently overviewed in association with their state-of-the-art progress. The prospect of phase-change materials for the future non-volatile electrical applications that are yet to be unraveled is finally envisaged.

/pdf/applications-of-phase-change-materials-in-electrical-regime-2hti578dys.pdf

Applications of Phase Change Materials in Electrical Regime From Conventional Storage Memory to Novel Neuromorphic Computing


 This work proposes a physical memristor () based new 4D chaotic system with 3D multi-scroll, no equilibrium point, spiking behaviour, coexistence bursting oscillation and multistability. Using this physical memristor-based chaotic system, a novel and efficient colour image encryption algorithm has been developed using a unique box scrambling method and bit-wise XOR operations. Many interesting and new dynamics of a material-based memristive chaotic system are reported here, like 3D multi-scroll chaotic attractors, bursting characteristics, multistability, a neuronal system like spiking behaviours etc. using Lyapunov spectrum and bifurcation plots. It is observed that the number of scrolls is changed with the total simulation time. This novel memristive chaotic system has limit cycles with controllable spikes and bursting oscillation. In addition, the system shows chaotic bursting oscillation under a different set of parameters and initial conditions. The coexistence of the bursting phenomena is studied here. The bursting and spiking characteristic is important for material-based memristors in neuromorphic applications. 3D Chaotic multi-scroll and multistability properties make the image encryption method more efficient and secure. Such characteristics are rare in physical memristor-based chaotic systems and using this, the image encryption algorithm is also rare in recent findings. Therefore, a new secure image encryption algorithm for colour images is proposed here, based on the unique box scrambling method, bitwise XOR operation and pseudo-random number generation using the proposed memristive chaotic system. Various tests like NPCR, UACI, histogram analysis, correlation study, information entropy analysis, robustness against external noise, etc. have been performed to check the algorithm's robustness and efficiency and test the capability to resist statistical and differential attacks.

A physical memristor-based chaotic system and its application in colour image encryption scheme

Phase‐change semiconductor is one of the best candidates for designing nonvolatile memory, but it has never been realized in organic semiconductors until now. Here, a phase‐changeable and high‐mobility organic semiconductor (3,6‐DATT) is first synthesized. Benefiting from the introduction of electrostatic hydrogen bond (S···H), the molecular conformation of 3,6‐DATT crystals can be reversibly modulated by the electric field and ultraviolet irradiation. Through experimental and theoretical verification, the tiny difference in molecular conformation leads to crystalline polymorphisms and dramatically distinct charge transport properties, based on which a high‐performance organic phase‐change memory transistor (OPCMT) is constructed. The OPCMT exhibits a quick programming/erasing rate (about 3 s), long retention time (more than 2 h), and large memory window (i.e., large threshold voltage shift over 30 V). This work presents a new molecule design concept for organic semiconductors with reversible molecular conformation transition and opens a novel avenue for memory devices and other functional applications.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202205694

Organic Phase‐Change Memory Transistor Based on an Organic Semiconductor with Reversible Molecular Conformation Transition

We investigate the morphological, electrical, magnetic, and resistive switching properties of (Co, Yb) co-ZnO for neuromorphic computing. By using hydrothermal synthesized nanoparticles and their corresponding sputtering target, we introduce Co and Yb into the ZnO structure, leading to increased oxygen vacancies and grain volume, indicating grain growth. This growth reduces grain boundaries, enhancing electrical conductivity and room-temperature ferromagnetism in Co and Yb-doped ZnO nanoparticles. We present a sputter-grown memristor with a (Co, Yb) co-ZnO layer between Au electrodes. Characterization confirms the ZnO layer's presence and 100 nm-thick Au electrodes. The memristor exhibits repeatable analog resistance switching, allowing manipulation of conductance between low and high resistance states. Statistical endurance tests show stable resistive switching with minimal dispersion over 100 pulse cycles at room temperature. Retention properties of the current states are maintained for up to 1000 seconds, demonstrating excellent thermal stability. A physical model explains the switching mechanism, involving Au ion migration during “set” and filament disruption during “reset.” Current–voltage curves suggest space-charge limited current, emphasizing conductive filament formation. All these results shows good electronic devices and systems towards neuromorphic computing.

Enhanced electrical and magnetic properties of (Co, Yb) co-doped ZnO memristor for neuromorphic computing

A novel design of patterned phase-change electrical probe memory with a continuous capping layer is proposed. To achieve ultra-high density, whilst having immunity to thermal and readout cross-talk interference, the devised memory is optimized to have a continuous 40 nm TiN underlayer, continuous 2 nm DLC capping layer and 5 nm Ge2Sb2Te5 layer patterned into numerous cells, separated by a 5 nm thick insulator region. The feasibility of using the designed device to achieve ultra-high density, superb anti-interference and a simplified fabrication process, has been demonstrated according to a developed full 3D electro-thermal and phase-change model.

Design of ultra-high storage density probe memory with patterned Ge2Sb2Te5 layer and continuous capping layer

Conventional von Newmann-based computers face severe challenges in the processing and storage of the large quantities of data being generated in the current era of “big data.” One of the most promising solutions to this issue is the development of an artificial neural network (ANN) that can process and store data in a manner similar to that of the human brain. To extend the limits of Moore’s law, memristors, whose electrical and optical behaviors closely match the biological response of the human brain, have been implemented for ANNs in place of the traditional complementary metal-oxide-semiconductor (CMOS) components. Based on their different operation modes, we classify the memristor family into electronic, photonic, and optoelectronic memristors, and review their respective physical principles and state-of-the-art technologies. Subsequently, we discuss the design strategies, performance superiorities, and technical drawbacks of various memristors in relation to ANN applications, as well as the updated versions of ANN, such as deep neutral networks (DNNs) and spike neural networks (SNNs). This paper concludes by envisioning the potential approaches for overcoming the physical limitations of memristor-based neural networks and the outlook of memristor applications on emerging neural networks.

Overview of Memristor-Based Neural Network Design and Applications

Phase-change random access memory (PCRAM) is widely regarded as one of the most promising candidates to replace Flash memory as the next generation of non-volatile memories due to its high-speed and low-power consumption characteristics. Recent advent of the blade-type PCRAM with low programming current merit further confirms its prospects. The thermoelectric effects existing inside the PCRAM devices have always been an important factor that determines the phase-transformation kinetics due to a fact that it allows PCRAM to have electric polarity dependent characteristics. However, the potential physics governing the thermoelectric effects for blade-type PCRAM device still remains vague. We establish a three-dimensional (3D) electro-thermal and phase-transformation model to study the influences of thermal boundary resistance (TBR) and device scaling on the thermoelectric effects of the blade-type PCRAM during its “RESET” operation. Our research shows that the presence of TBR significantly improves the electric polarity-dependent characteristics of the blade-type PCRAM, and such polarity-dependent characteristic is found immune to the scaling of the device. It is therefore possible to optimize the thermoelectric effects of the blade-type PCRAM through appropriately tailoring the TBR parameters, thus further lowering resulting power consumption.

Impact of Thermal Boundary Resistance on Thermoelectric Effects of the Blade-Type Phase-Change Random Access Memory Device

Phase-change electrical probe memory has recently attained considerable attention owing to its profound potential for next-generation mass and archival storage devices. To encourage more talented researchers to enter this field and thereby advance this technology, this paper first introduces approaches to induce the phase transformation of chalcogenide alloy by probe tip, considered as the root of phase-change electrical probe memory. Subsequently the design rule of an optimized architecture of phase-change electrical probe memory is proposed based on a previously developed electrothermal and phase kinetic model, followed by a summary of the state-of-the-art phase-change electrical probe memory and an outlook for its future prospects.

Overview of Phase-Change Electrical Probe Memory.

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Overview of Memristor-Based Neural Network Design and Applications

Impact of Thermal Boundary Resistance on Thermoelectric Effects of the Blade-Type Phase-Change Random Access Memory Device

Overview of Phase-Change Electrical Probe Memory.