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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. The 3D semiconductor fabrication technology requires an “ovonic threshold switching (OTS)” selector device to control the open and shut of each memory unit. The physics of these materials, however, has not been well understood due to complex structure of chalcogenide glass. The authors focus on the defect states which are responsible for OTS behaviors via machine learning of the large amount of structure data. The physical origin of OTS is revealed and the properties of these materials can be predicted, paving the way for the materials design toward high‐density memory integration.

Phase‐change material (PCM) devices are one of the most mature nonvolatile memories. However, their high power consumption remains a bottleneck problem limiting the data storage density. One may drastically reduce the programming power by patterning the PCM volume down to nanometer scale, but that route incurs a stiff penalty from the tremendous cost associated with the complex nanofabrication protocols required. Instead, here a materials solution to resolve this dilemma is offered. The authors work with memory cells of conventional dimensions, but design/exploit a PCM alloy that decomposes into a heterogeneous network of nanoscale crystalline domains intermixed with amorphous ones. The idea is to confine the subsequent phase‐change switching in the interface region of the crystalline nanodomain with its amorphous surrounding, forming/breaking “nano‐bridges” that link up the crystalline domains into a conductive path. This conductive‐bridge switching mechanism thus only involves nanometer‐scale volume in programming, despite of the large areas in contact with the electrodes. The pore‐like devices based on spontaneously phase‐separated Ge13Sb71O16 alloy enable a record‐low programming energy, down to a few tens of femtojoule. The new PCM/fabrication is fully compatible with the current 3D integration technology, adding no expenses or difficulty in processing. The high‐power consumption in phase‐change memory remains a bottleneck problem limiting data storage density. Here, a conductive‐bridge switching mechanism with a self‐decomposed Ge13Sb71O16 alloy is demonstrated, which results in a heterogeneous network of nanoscale amorphous/crystalline domains, confining the phase‐change switch in a “nano‐bridge” at the interface between amorphous and crystalline domains. A record‐low programming energy is achieved in conventional‐sized devices.

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. This work demonstrates that Cr2Ge2Te6 preserves the spin‐polarized nature in the amorphous phase, but undergoes a magnetic transition to a spin glass state below 20 Kelvin. Ab initio simulations indicate that the presence of angular disorder and bonding distortions weakens the magnetic order in amorphous Cr2Ge2Te6, leading to the coexistence of ferromagnetic and antiferromagnetic couplings.

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. The 4 nm‐thick monoatomic Sb film enables the ultralow resistance drift coefficient v, ranging from ≈10−4 to ≈10−3, which will benefit a further promotion in multibit programming accuracy to develop high‐capacity universal memory and high‐efficiency computing chips.

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. Organic phase‐change semiconductor is one of the most promising candidates to design nonvolatile memories, which is not realized until now. This work first reports a novel principle to reversibly change the crystal phase of an organic semiconductor by modulating the molecular conformation transition, based on which high‐performance organic phase‐change memory transistors are constructed.

Chalcogenides, despite their versatile functionality, share a notably similar local structure in their amorphous states. Particularly in electronic phase‐change memory applications, distinguishing these glasses from neighboring compositions that do not possess memory capabilities is inherently difficult when employing traditional analytical methods. This has led to a dilemma in materials design since an atomistic view of the arrangement in the amorphous state is the key to understanding and optimizing the functionality of these glasses. To tackle this challenge, we present a machine learning (ML) approach to separate electronic phase‐change materials (ePCMs) from other chalcogenides, based upon subtle differences in the short‐range order inside the glassy phase. Leveraging the established structure–property relations in chalcogenide glasses, we select suitable features to train accurate machine learning models, even with a modestly sized dataset. The trained model accurately discerns the critical transition point between glass compositions suitable for use as ePCMs and those that are not, particularly for both GeTe–GeSe and Sb2Te3–Sb2Se3 materials, in line with experiments. Furthermore, by extracting the physical knowledge that the ML model has offered, we pinpoint three pivotal structural features of amorphous chalcogenides, that is, the bond angle, packing efficiency, and the length of the fourth bond, which provide a map for materials design with the ability to “predict” and “explain”. All three of the above features point to the smaller Peierls‐like distortion and more well‐defined octahedral clusters in amorphous ePCMs than non‐ePCMs. Our study delves into the mechanisms shaping these structural attributes in amorphous ePCMs, yielding valuable insights for the AI‐powered discovery of novel materials. Despite their diverse applications, chalcogenides share similar local structures in amorphous states, making it challenging to identify electronic phase‐change memory (ePCM) materials using traditional methods. We developed a machine learning approach to distinguish ePCMs from non‐ePCMs by analyzing subtle structural differences, revealing key features like bond angle and packing efficiency that guide material design. This work provides a roadmap for AI‐driven discovery of novel functional materials.

In this paper, gradual and symmetrical long-term potentiation (LTP) and long-term depression (LTD) were achieved by applying the optimal electrical pulse condition of the interfacial phase-change memory (iPCM) based on a superlattice (SL) structure fabricated by stacking GeTe/Sb2Te3 alternately to implement an artificial synapse in neuromorphic computing. Furthermore, conventional phase-change random access memory (PCRAM) based on a Ge–Sb–Te (GST) alloy with an identical bottom electrode contact size was fabricated to compare the electrical characteristics. The results showed a reduction in the reset energy consumption of the GeTe/Sb2Te3 (GT/ST) iPCM by more than 69% of the GST alloy for each bottom electrode contact size. Additionally, the GT/ST iPCM achieved gradual conductance tuning and 90.6% symmetry between LTP and LTD with a relatively unsophisticated pulse scheme. Based on the above results, GT/ST iPCM is anticipated to be exploitable as a synaptic device used for brain-inspired computing and to be utilized for next-generation non-volatile memory.

Emerging nonvolatile memory (eNVM) devices are pushing the limits of emerging applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Among several alternatives, phase change memory, spin-transfer torque random access memory, and resistive random-access memory (RRAM) are major emerging technologies. This review explains all varieties of prototype and eNVM devices, their challenges, and their applications. A performance comparison shows that it is difficult to achieve a “universal memory” which can fulfill all requirements. Compared to other emerging alternative devices, RRAM technology is showing promise with its highly scalable, cost-effective, simple two-terminal structure, low-voltage and ultra-low-power operation capabilities, high-speed switching with high-endurance, long retention, and the possibility of three-dimensional integration for high-density applications. More precisely, this review explains the journey and device engineering of RRAM with various architectures. The challenges in different prototype and eNVM devices is disused with the conventional and novel application areas. Compare to other technologies, RRAM is the most promising approach which can be applicable as high-density memory, storage class memory, neuromorphic computing, and also in hardware security. In the post-CMOS era, a more efficient, intelligent, and secure computing system is possible to design with the help of eNVM devices.

Phase change memory cells composed of nanocrystalline Ge2Sb2Te5 with a heater diameter of 10 nm and Ge2Sb2Te5 thickness of 100 nm are studied by using two-dimensional finite element simulations with COMSOL Multiphysics. The nanocrystalline Ge2Sb2Te5 is emulated by using a mesh-based model incorporating crystalline grains of random size and location embedded in the amorphous media. The material parameters are modelled with temperature dependency from 300 to 1000 K, including electrical resistivity, thermal conductivity, electric field breakdown and Seebeck coefficient. This model is shown to capture the cycle-to-cycle and device-to-device variability in phase change memory cells.