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The work is devoted to the study of the characteristics of the state control of a thin-film element based on a phase-change GeTe material. The properties of such an element have been controlled by the action of sequences of ultrashort laser pulses. This action leads to a rapid heating of the thin film element and provides a phase transition between states with a resistance different by several orders of magnitude. The dynamics of the resistance was studied using a high speed oscilloscope according to the scheme where the element under study was the voltage divider arm of a highly stable source. Three different types of conductivity switching were observed for 100 nm thin films. For low energy laser radiation, several distinct states were obtained in which the material film has predominantly semiconducting properties. As the energy of the optical pulses increases, the number of possible stable states determined by the specific conductivity of the material decreases to two, one of which (low resistance) is exclusively metallic properties. In all cases, the time taken to switch to a stable state does not exceed a few tens of nanoseconds for films up to 100 nm thick. The study has demonstrated that the structures described can be used to implement optically controlled memristive elements. In addition, the large number of possible allowable specific resistances of the element will make it possible to use it to increase the information capacity of memory cells based on phase-change materials or to implement optoelectronic neuromorphic systems.

ABSTRACTPhase-change memory (PCM), recently developed as the storage-class memory in a computer system, is a new non-volatile memory technology. In addition, the applications of PCM in a non-von Neumann computing, such as neuromorphic computing and in-memory computing, are being investigated. Although PCM-based devices have been extensively studied, several concerns regarding the electrical, thermal, and structural dynamics of phase-change devices remain. In this article, aiming at PCM devices, a comprehensive review of PCM materials is provided, including the primary PCM device mechanics that underpin read and write operations, physics-based modeling initiatives and experimental characterization of the many features examined in nanoscale PCM devices. Finally, this review will propose a prognosis on a few unsolved challenges and highlight research areas of further investigation.

Abstract Phase‐change random‐access memory (PCRAM) devices suffer from pronounced resistance drift originating from considerable structural relaxation of phase‐change materials (PCMs), which hinders current developments of high‐capacity memory and high‐parallelism computing that both need reliable multibit programming. This work realizes that compositional simplification and geometrical miniaturization of traditional GeSbTe‐like PCMs are feasible routes to suppress relaxation. While to date, the aging mechanisms of the simplest PCM, Sb, at nanoscale, have not yet been unveiled. Here, this work demonstrates that in an optimal thickness of only 4 nm, the thin Sb film can enable a precise multilevel programming with ultralow resistance drift coefficients, in a regime of ≈10−4–10−3. This advancement is mainly owed to the slightly changed Peierls distortion in Sb and the less‐distorted octahedral‐like atomic configurations across the Sb/SiO2 interfaces. This work highlights a new indispensable approach, interfacial regulation of nanoscale PCMs, for pursuing ultimately reliable resistance control in aggressively‐miniaturized PCRAM devices, to boost the storage and computing efficiencies substantially.

Silicon photonics (SiPh) technology has facilitated the deployment of integrated photonics across different application domains, from ultra-fast communication in Datacom applications to energy-efficient optical computation in emerging hardware accelerators for machine learning. More recently, the integration of SiPh and phase change materials has created a unique opportunity to realize adaptable, reconfigurable, and programmable photonic platforms. In particular, the nonvolatile programmability in phase change materials has made them a promising candidate for implementing photonic memory cells and architectures. Accordingly, photonic memory systems and even in-memory photonic computing paradigms are on the rise, especially given their potential for improving data access in electronic and photonic processors. However, there are still many challenges in the design and fabrication of phase-change photonic integrated circuits, which need to be addressed. This article presents a comprehensive survey on the recent advances and challenges for the integration of phase change materials with contemporary photonic devices while focusing on the photonic memory application. In particular, we explore phase-change photonic memory from the material level to the architecture level by presenting an overview of different material-level characteristics of phase change materials with their optical, electrical, and thermal properties as well as their integration into SiPh devices and photonic memory architectures and their application for in-memory photonic computing. We also present a comparison with electronic memory and discuss open research challenges that must be addressed to further advance phase-change photonic memory towards successful integration into emerging computing systems.

Abstract The layered crystal structure of Cr2Ge2Te6 shows ferromagnetic ordering at the two‐dimensional limit, which holds promise for spintronic applications. However, external voltage pulses can trigger amorphization of the material in nanoscale electronic devices, and it is unclear whether the loss of structural ordering leads to a change in magnetic properties. Here, it is demonstrated that Cr2Ge2Te6 preserves the spin‐polarized nature in the amorphous phase, but undergoes a magnetic transition to a spin glass state below 20 K. Quantum‐mechanical computations reveal the microscopic origin of this transition in spin configuration: it is due to strong distortions of the CrTeCr bonds, connecting chromium‐centered octahedra, and to the overall increase in disorder upon amorphization. The tunable magnetic properties of Cr2Ge2Te6 can be exploited for multifunctional, magnetic phase‐change devices that switch between crystalline and amorphous states.

Phase-change memory (PCM) is a promising non-volatile memory device due to its attractive properties such as fast access time and byte-addressability. However, PCM is still difficult to be used as a main memory because of its weakness in endurance and write disturbance. Conventional wear-leveling algorithms have attempted to handle short endurance, but they have not addressed the issue of write disturbance even though both issues are caused by write operations to PCM. This paper proposes a wear-leveling algorithm that addresses not only the endurance, but also the write disturbance. From the observation that the write disturbance errors and short endurance issues mostly take place in hot addresses, the proposed algorithm first detects hot addresses and maps them to ‘hot’ regions, which are customized memory regions designed to be robust to write disturbance errors and to support effective wear-leveling. The ‘hot’ region is not fixed to a specific part, but it moves over an entire memory space to make every cell belong to ‘hot’ regions in a uniform manner. On the other hand, cold addresses are mapped in ‘normal’ memory regions by simple linear mapping to reduce the hardware overhead. The proposed algorithm can reduce the write disturbance errors by more than 70% with only a slight instruction per cycle (IPC) degradation. Moreover, the wear-leveling performance is also enhanced by more than 3% compared to other wear-leveling algorithms.

Abstract 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.

Multi-Level Cell Phase Change Memory (MLC PCM) is a non-volatile memory technology that promises high-density data storage. However, MLC PCM suffers from long write latency because changing the state of each memory cell takes a long time and the number of cells that can be written concurrently is limited due to the relatively large size and power consumption of the write drivers. MLC PCM typically divides the write data into multiple cell groups to program cells in batches. The group mapping has a significant impact on the overall latency of write requests. Prior work proposed a group mapping technique for single-level cell (SLC) PCM based on the data pattern of a set of applications. In this work, a larger design space of group mapping is explored for MLC PCM. The specific group mapping for each benchmark is dynamically customized. Our technique leads to a 13.14% reduction in write latency compared to the state-of-the-art method (Du et al., 2013) and shows good temporal consistence in different benchmarks.

Abstract Phase‐change memory (PCM) has been developed for three‐dimensional (3D) data storage devices, posing huge challenges to the thermal stability and reliability of PCM. However, the low thermal stability of Ge2Sb2Te5 (GST) limits further application. Here, we demonstrate PCM based on In0.9Ge2Sb2Te5 (IGST) alloy, showing 180°C 10‐years data retention, 6 ns set speed, one order of magnitude longer life time, and 75% reduced power consumption compared to GST‐based device. The In can occupy the cationic positions and the In‐Te octahedrons with good phase‐change properties can geometrically match well with the host Ge‐Te and Sb‐Te octahedrons, acting as nucleation centers to boost the set speed and enhance the endurance of IGST device. Introducing stable matched phase‐change octahedrons can be a feasible way to achieve practical PCMs.

A fully silicon‐integrated restricted Boltzmann machine (RBM) with an event‐driven contrastive divergence (eCD) training algorithm is implemented using novel stochastic leaky integrate‐and‐fire (LIF) neuron circuits and six‐transistor/2‐PCM‐resistor (6T2R) synaptic unit cells on 90 nm CMOS technology. To elaborate, designed a bidirectional, asynchronous, and parallel pulse‐signaling scheme over an analog‐weighted phase‐change memory (PCM) synapse array to enable spike‐timing‐dependent plasticity (STDP) as a local weight update rule based on eCD is designed. Building upon the initial version of this work, significantly more experimental details are added, such as the on‐chip characterization results of LIF and backward‐LIF (BLIF) and stochasticity of our random walk circuitry. The experimental characterization of these on‐chip stochastic neuron circuits shows a reasonable symmetricity between LIF and BLIF as well as the necessary stochasticity for spiking RBM operation. Fully hardware‐based image classification recorded 93% on‐chip training accuracy from 100 handwritten MNIST digit images. In addition, we experimentally demonstrated the generative characteristics of the RBM by reconstructing partial patterns on hardware. As each synapse and neuron execute its computations in an asynchronous and fully parallel fashion, the chip can perform data‐intensive machine learning (ML) tasks in a power‐efficient manner and take advantage of the sparseness of spiking.

Abstract The recent development of three‐dimensional semiconductor integration technology demands a key component—the ovonic threshold switching (OTS) selector to suppress the current leakage in the high‐density memory chips. Yet, the unsatisfactory performance of existing OTS materials becomes the bottleneck of the industrial advancement. The sluggish development of OTS materials, which are usually made from chalcogenide glass, should be largely attributed to the insufficient understanding of the electronic structure in these materials, despite of intensive research in the past decade. Due to the heavy first‐principles computation on disordered systems, a universal theory to explain the origin of mid‐gap states (MGS), which are the key feature leading to the OTS behavior, is still lacking. To avoid the formidable computational tasks, we adopt machine learning method to understand and predict MGS in typical OTS materials. We build hundreds of chalcogenide glass models and collect major structural features from both short‐range order (SRO) and medium‐range order (MRO) of the amorphous cells. After training the artificial neural network using these features, the accuracy has reached ~95% when it recognizes MGS in new glass. By analyzing the synaptic weights of the input structural features, we discover that the bonding and coordination environments from SRO and particularly MRO are closely related to MGS. The trained model could be used in many other OTS chalcogenides after minor modification. The intelligent machine learning allows us to understand the OTS mechanism from vast amount of structural data without heavy computational tasks, providing a new strategy to design functional amorphous materials from first principles.

Abstract Phase-change memory (PCM) has considerable promise for new applications based on von Neumann and emerging neuromorphic computing systems. However, a key challenge in harnessing the advantages of PCM devices is achieving high-speed operation of these devices at elevated temperatures, which is critical for the efficient processing and reliable storage of data at full capacity. Herein, we report a novel PCM device based on Ta-doped antimony telluride (Sb2Te), which exhibits both high-speed characteristics and excellent high-temperature characteristics, with an operation speed of 2 ns, endurance of > 106 cycles, and reversible switching at 140 °C. The high coordination number of Ta and the strong bonds between Ta and Sb/Te atoms contribute to the robustness of the amorphous structure, which improves the thermal stability. Furthermore, the small grains in the three-dimensional limit lead to an increased energy efficiency and a reduced risk of layer segregation, reducing the power consumption and improving the long-term endurance. Our findings for this new Ta–Sb2Te material system can facilitate the development of PCMs with improved performance and novel applications.

The dimensions of amorphized regions in phase-change memory cells are critical parameters to design devices for different applications. However, these dimensions are difficult to be determined by direct imaging. In this work, the length of amorphized regions in multiple identical Ge2Sb2Te5 (GST) line cells was extracted from electrical measurements. After each cell was programmed to an amorphous state, a sequence of increasing-amplitude post-reset voltage pulses separated by low-amplitude read DC sweeps was applied. When a post-reset voltage pulse with sufficient amplitude was applied to a given cell, the measured current and the post-pulse resistance increased drastically, indicating that the cell re-amorphized after threshold switching, melting, and quenching. The amorphized length was calculated using the measured voltage at which the threshold switching occurred and the expected drifted threshold field at that time. The measured threshold voltage values and, hence, the extracted amorphized length, generally increase linearly with the programmed resistance levels. However, significant variability arises from the intrinsically unique crystallization and amorphization processes in these devices. For example, cells programmed to an amorphous resistance of approx. 50 MΩ show threshold voltage values of 5.5–7.5 V, corresponding to amorphized length values of 290–395 nm. This unpredictable programming feature in phase-change memory devices can be utilized in hardware security applications.

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has motivated the search for novel computing architectures targeting this application. A computational memory unit with nanoscale resistive memory devices organized in crossbar arrays could store the synaptic weights in their conductance states and perform the expensive weighted summations in place in a non-von Neumann manner. However, updating the conductance states in a reliable manner during the weight update process is a fundamental challenge that limits the training accuracy of such an implementation. Here, we propose a mixed-precision architecture that combines a computational memory unit performing the weighted summations and imprecise conductance updates with a digital processing unit that accumulates the weight updates in high precision. A combined hardware/software training experiment of a multilayer perceptron based on the proposed architecture using a phase-change memory (PCM) array achieves 97.73% test accuracy on the task of classifying handwritten digits (based on the MNIST dataset), within 0.6% of the software baseline. The architecture is further evaluated using accurate behavioral models of PCM on a wide class of networks, namely convolutional neural networks, long-short-term-memory networks, and generative-adversarial networks. Accuracies comparable to those of floating-point implementations are achieved without being constrained by the non-idealities associated with the PCM devices. A system-level study demonstrates 172 × improvement in energy efficiency of the architecture when used for training a multilayer perceptron compared with a dedicated fully digital 32-bit implementation.

Ge-rich Ge–Sb–Te compounds are attractive materials for future phase change memories due to their greater crystallization temperature as it provides a wide range of applications. Herein, we report the self-assembled Ge-rich Ge–Sb–Te/Sb2Te3 core-shell nanowires grown by metal-organic chemical vapor deposition. The core Ge-rich Ge–Sb–Te nanowires were self-assembled through the vapor–liquid–solid mechanism, catalyzed by Au nanoparticles on Si (100) and SiO2/Si substrates; conformal overgrowth of the Sb2Te3 shell was subsequently performed at room temperature to realize the core-shell heterostructures. Both Ge-rich Ge–Sb–Te core and Ge-rich Ge–Sb–Te/Sb2Te3 core-shell nanowires were extensively characterized by means of scanning electron microscopy, high resolution transmission electron microscopy, X-ray diffraction, Raman microspectroscopy, and electron energy loss spectroscopy to analyze the surface morphology, crystalline structure, vibrational properties, and elemental composition.

Superlattice-like (SLL) phase-change film is considered to be a promising phase-change material because it provides more controllabilities for the optimization of multiple performances of phase-change films. However, the mechanism by which SLL structure affects the properties of phase-change films is not well-understood. Here, four SLL phase-change films [Ge8Sb92(15 nm)/Ge (x nm)]3 with different x are fabricated. Their behaviors of crystallization are investigated by measuring sheet resistance and coherent phonon spectroscopy, which show that the crystallization temperature (TC) of these films increases anomalously with x, rather than decreases as the interfacial effects model predicted. A new stress effect is proposed to explain the anomalous increase in TC with x. Raman spectroscopy reveals that Raman shifts of all phonon modes in SLL films deviate from their respective standard Raman shifts in stress-free crystalline films, confirming the presence of stress in SLL films. It is also shown that tensile and compressive stresses exist in Ge and Ge8Sb92 layers, respectively, which agrees with the lattice mismatch between the Ge and Ge8Sb92 constituent layers. It is also found that the stress reduces with increasing x. Such a thickness dependence of stress can be used to explain the increase in crystallization temperature of four SLL films with x according to stress-enhanced crystallization. Our results reveal a new mechanism to affect the crystallization behaviors of SLL phase-change films besides interfacial effect. Stress and interfacial effects actually coexist and compete in SLL films, which can be used to explain the reported anomalous change in crystallization temperature with the film thickness and cycle number of periods in SLL phase-change films.

Chalcogenide-based phase-change materials play a prominent role in information technology. In spite of decades of research, the details of electrical transport in these materials are still debated. In this article, we present a unified model based on multiple-trapping transport together with 3D Poole–Frenkel emission from a two-center Coulomb potential. With this model, we are able to explain electrical transport both in as-deposited phase-change material thin films, similar to experimental conditions in early work dating back to the 1970s, and in melt-quenched phase-change materials in nanometer-scale phase-change memory devices typically used in recent studies. Experimental measurements on two widely different device platforms show remarkable agreement with the proposed mechanism over a wide range of temperatures and electric fields. In addition, the proposed model is able to seamlessly capture the temporal evolution of the transport properties of the melt-quenched phase upon structural relaxation.