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Chalcogenide is a proven phase change material used in re-writeable CDs and DVDs. This material changes phases, reversibly and quickly, between an amorphous state that is dull in appearance and electrically high in resistance, and a polycrystalline state that is highly reflective and low in resistance. The application of this commercially proven technology to create dense, high-speed, non-volatile semiconductor memories is discussed.
Strain engineering is vital for tuning the optical and spin properties of solid-state color centers, enhancing spin coherence and compensating emission wavelength shift. Here, we develop an all-optical approach to directly modify the local strain of color centers at the nanoscale by migrating the nearby defect. High-power pulsed optical irradiation triggers defect migration, which subsequently leads to the redistribution of the local crystal lattice of the host material. This redistribution alters the strain experienced by nearby color centers. Using silicon-vacancy centers in diamond, we validate this method and demonstrate a ground state splitting enhancement of up to 1.8 THz. Unlike conventional methods, our approach requires no external fields or nanostructure modifications, enabling non-volatile strain control and optical memory functionality across wide temperature ranges. Its local, permanent nature offers a scalable path for enhancing spin coherence in large-scale quantum systems and has potential applications in photonic machine learning.
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Hafnia-based ferroelectrics hold promise to reduce energy demand for computing by enabling compute-in-memory and as non-volatile memories. The ferroelectric phase in this material system is, in part, stabilized by oxygen vacancies. While oxygen vacancies may be a necessity for phase stability, they limit device endurance through diffusion and accumulation into conducting channels. Herein, it is shown that oxygen diffusion is spatially variable within individual grains of ferroelectric hafnium zirconium oxide (HZO). Using 18 O tracers and finite difference modeling, it is shown that grain boundaries and regions near electrode interfaces allow for relatively rapid oxygen diffusion, with values as much as 10 4 larger than the grain cores. Further, the selection of electrode material affects the diffusion coefficients across all microstructural regions. HZO films in contact with TiN electrodes result in more oxygen-deficient HZO films and higher oxygen diffusion coefficients. Tungsten electrodes result in fewer vacancies and lower diffusion coefficients. Diffusion activation energy differences between the HZO with the two electrodes is reconciled by differing populations of charged and uncharged oxygen vacancies. This insight into the local vacancy populations and diffusion pathways provides a platform for designing hafnia-based films, deposition processes, and integration strategies to reduce vacancy gradients and improve performance.
The ramp-reversal memory (RRM) effect in metal–insulator transition metal oxides (TMOs), a non-volatile resistance change induced by repeated temperature cycling, has attracted considerable interest in neuromorphic computing and non-volatile memory devices. Our previous defect motion model successfully explained RRM in vanadium dioxide (VO 2 ), capturing observed critical temperature shifts and memory accumulation throughout the sample. However, this approach lacked interactions between metallic and insulating domains. Here, we extend our model by combining a correlated Random Field Ising Model with defect diffusion-segregation, enabling accurate hysteresis modeling while predicting the relationship between RRM and domain interactions. Our simulations demonstrate that the maximum RRM occurs when the turnaround temperature approaches the inflection point. This peak in RRM vs. turnaround temperature is consistent with prior transport measurements, as well as our own optical measurements reported here. Significantly, we find that increasing nearest-neighbor interactions enhances the maximum memory effect, thus providing a clear mechanism for optimizing RRM performance. Since our model employs minimal assumptions, we predict that RRM should be a widespread phenomenon in materials exhibiting patterned phase coexistence of electronic domains. This work not only advances fundamental understanding of memory behavior in TMOs but also establishes a much-needed theoretical framework for optimizing device applications.
This work presents a novel approach to configure 2T-nC ferroelectric RAM (FeRAM) for performing single cell logic-in-memory operations, highlighting its advantages in energy-efficient computation over conventional DRAM-based approaches. Unlike conventional 1T-1C dynamic RAM (DRAM), which incurs refresh overhead, 2T-nC FeRAM offers a promising alternative as a non-volatile memory solution with low energy consumption. Our key findings include the potential of quasi-nondestructive readout (QNRO) sensing in 2T-nC FeRAM for logic-in-memory (LiM) applications, demonstrating its inherent capability to perform inverting logic without requiring external modifications, a feature absent in traditional 1T-1C DRAM. We successfully implement the MINORITY function within a single cell of 2T-nC FeRAM, enabling universal NAND and NOR logic, validated through SPICE simulations and experimental data. Additionally, the research investigates the feasibility of 3D integration with 2T-nC FeRAM, showing substantial improvements in storage and computational density, facilitating bulk-bitwise computation. Our evaluation of eight real-world, data-intensive applications reveals that 2T-nC FeRAM achieves 2× higher performance and 2.5× lower energy consumption compared to DRAM. Furthermore, the thermal stability of stacked 2T-nC FeRAM is validated, confirming its reliable operation when integrated on a compute die. These findings emphasize the advantages of 2T-nC FeRAM for LiM, offering superior performance and energy efficiency over conventional DRAM.
Processing-in-Memory (PIM) is emerging as a practical path to overcome the limitations of traditional von Neumann architectures. At its core, PIM systems implement computing primitives such as logic operations and multiply-accumulate acceleration through compute-in-memory, near-memory processing, or hybrid designs. The role of memory cells varies widely across technologies, acting as inputs, outputs, or analog accumulators through bit-lines and sense amplifiers. This diversity creates trade-offs in precision, bandwidth, latency, and programmability, making it difficult to build a unified understanding on the progress of the field. In this survey, we organize recent advances of PIM into three areas. First, we discuss the progress on the architectural optimizations of PIM and its integration with both DRAM and emerging non-volatile memories. Second, we examine how PIM is being used to accelerate key computing domains, including generative AI workloads and high-performance kernels, along with new approaches. Third, we highlight the growing adoption of PIM in computational sciences, where it is being applied to solve interdisciplinary problems such as genome analysis, mRNA quantification, mass spectrometry, quantum circuit simulation, wave modeling, and secure computation. Finally, we synthesize the major challenges that continue to slow PIM adoption, including manufacturing constraints, power delivery, thermal reliability, data consistency, runtime and memory-management coordination, and the difficulty of building portable software abstractions without sacrificing commercial viability. This work provides an updated, structured perspective on PIM’s potential across computing and computational sciences and the barriers that must be solved for it to reach its full impact.
The explosive growth in data collection and the need to process it efficiently, as well as the desire to automate increasingly complex tasks in transportation, medical care, manufacturing, security and many other fields have motivated a growing interest in neuromorphic computing. Unlike the binary, transistorbased ON/OFF logic gates and separate logic and memory functionalities employed in digital computing, neuromorphic computing is inspired by animal brains that use interconnected synapses and neurons to perform processing, storage and transmission of information at the same location, while only consuming ~20 W or less of power. Motivated by the brain’s efficiency, adaptability, self-learning and resiliency qualities, neuromorphic computing can be broadly defined as an approach to processing and storing information using hardware and algorithms inspired by models of biological neural systems. Present research in neuromorphic computing encompasses approaches that vary significantly in their degree of neuro-inspiration, from systems that only incorporate features such as asynchronous, event-driven operation or use crossbar arrays of non-volatile memory (NVM) elements to accelerate deep neural networks (DNNs), to designs that embrace the extreme parallelism, sparsity, reconfigurability, adaptability, complexity and stochasticity observed in nervous systems. The term ‘neuromorphic’ computing is often credited to Carver Mead, who in the 1980s investigated Si-based analog electronics to replicate functions of the animal retina. Earlier important advances in this field include the work of Frank Rosenblatt, who proposed the concept of the perceptron, Bernard Widrow, who used this concept to build one of the first analog neural networks, the Adaline and many other researchers (see ref. 6 for an historical perspective on neuromorphic computing). With the recent increase in the use of artificial intelligence and large language models, and rising concerns over the associated energy costs, interest in neuromorphic hardware has expanded rapidly. According to some estimates, driven largely by the drastic growth in the training use of artificial intelligence (AI) models using the current computing architectures, the energy cost of computing is projected to reach the energy supply worldwide by 2045. Furthermore, while this is not a realistic outcome, it means that, if more efficient computing technologies are not developed -- soon -- the world will soon become one where demand for energy and market constraints limit the continued increase of societal access to AI and cloud services from data centers. Data centers used for training and use of these models consume hundreds of terawatt hours of electricity, already past 4% of the US electricity demand.
HfO 2 -based ferroelectric materials have emerged as leading candidates for next-generation non-volatile memory technologies, owing to their nanoscale robust ferroelectricity and complementary metal–oxide–semiconductor (CMOS) compatibility. However, challenges and debates persist in advancing and comprehensively understanding their ferroelectric behavior. In particular, conventional approaches typically regard non-ferroelectric phases as detrimental and primarily focus on suppressing their formation, yet overlooking their potentially synergistic contributions—particularly those of the tetragonal (T) phase. Here, we unambiguously clarify the beneficial role of the T-phase and introduce a phase-boundary engineering strategy that deliberately harnesses it to enhance ferroelectricity in HfO 2 films. By stabilizing optimal coherent boundaries between ferroelectric orthorhombic (O) and T phases in epitaxial La-doped HfO 2 films, we achieve significant improvements in ferroelectric properties—doubling the remanent polarization (P r ∼ 30 µC/cm 2 ) and substantially reducing the coercive field (E c ∼ 3 MV/cm) by 30% compared to low-La doped samples without such boundaries. Atomic-scale electron microscopy reveals the structural nature of the atomically sharp, coherent O–T boundaries. Combined with deep-learning enhanced molecular dynamics simulations, our results unravel that these boundaries facilitate intermediate polarization states that lower the switching energy barrier. Consequently, phase coexistence shifts from an inherent drawback to a tunable design element, offering a broadly applicable route to ultra-low-power HfO 2 -based nanoelectronics.
The well-known classification of glass-forming melts into fragile and strong liquids has several notable exceptions, including water, silica, and certain phase-change materials (PCMs). These exceptional fluid systems exhibit a fragile-to-strong transition (FST) upon cooling: a transformation from a high-temperature liquid with fast atomic dynamics, low viscosity, and low flow activation energy, to a viscous supercooled melt with high energy barriers near the glass transition temperature T g . This behavior is critically important for non-volatile memories, photonic tensor cores, reconfigurable metamaterials, and other devices, that use PCMs, enabling nanosecond-scale crystallization in the fragile regime and long data retention in the strong regime near or below T g . A significant structural transformation is expected between these two viscosity regimes, along with a semiconductor-metal (SC-M) transition upon heating, driven by high internal pressure and associated density increase. By applying high external pressure to the canonical low-conducting chalcogenide melt As 2 S 3 , we observed both the FST and the SC-M transition, occurring simultaneously within the same domain of the P, T−phase space. These findings suggest that the FST is not limited to a few exceptional liquids but is a common phenomenon, at least in systems that exhibit melt metallization within specific regions of their P, T−phase diagrams.
Damage evolution and phase stability in defective β-Ga 2 O 3 and an irradiation-converted γ-Ga 2 O 3 layer have been studied under ionizing irradiation at 300 K. By exploring athermal nonequilibrium processes in β-Ga 2 O 3 , we succeed in identifying a self-healing mechanism that enables recovery pre-existing damage, characterized by a recovery cross-section of ~0.17 nm 2 . Remarkably, this study further demonstrates that the crystallinity of the irradiation-converted γ-Ga 2 O 3 layer improves under ionizing irradiation. More importantly, X-ray diffraction analysis reveals that the highly-strained 𝛾 -phase transforms into a highly-crystalline structure without film disintegration, contrasting to that reported for isochronal annealing at 1000 K. The inelastic thermal spike calculations provide insights into the important effects of energy transfer to electrons in reordering the local atomic arrangement of both defective β- Ga 2 O 3 and 𝛾-Ga 2 O 3 . This behavior suggests a pathway for low-temperature crystallization, offering a promising strategy for fabricating ultrahigh-speed non-volatile memory devices.
A deeper understanding of resistive switching (RS) in 2D materials is essential for advancing neuromorphic computing. The Dissociation-Diffusion-Adsorption (DDA) model offers a useful framework for probing RS mechanisms in non-volatile memory (NVM) and in-memory computing. We have employed first-principles density functional theory (DFT) to explore dissociation, diffusion, and adsorption phenomena within the DDA model, focusing on the interactions between exemplary metal atoms (Au, Ag, Cu) and monolayer MoS 2 . Nudged elastic band (NEB) calculations evaluated diffusion barriers in pristine and sulfur-vacancy MoS 2 . Charged systems were modeled to assess the impact of applied bias on migration pathways. We also examined metal dissociation from bulk electrodes and adsorption at S vacancies. Ag/MoS 2 shows the lowest dissociation barrier (~0.034 eV), while Au and Cu exhibit similar values (~0.32 eV). These insights highlight Ag as a promising candidate for low-energy RS applications and provide guidance for optimizing switching efficiency in 2D memory devices.
With its atomically thin structure and intrinsic ferroelectric properties, heterodeformed bilayer hexagonal boron nitride (hBN) has gained prominence in next-generation non-volatile memory applications. However, studies to date have focused almost exclusively on small-twist bilayer hBN, leaving the question of whether ferroelectricity can persist under small heterostrain and large heterodeformation entirely unexplored. In this work, we establish the crystallographic origin of ferroelectricity in bilayer hBN configurations heterodeformed relative to high-symmetry configurations such as AA-stacking and 21.786789° twisted configurations (Σ7), using Smith normal form bicrystallography. We then demonstrate out-of-plane ferroelectricity in bilayer hBN across configurations vicinal to both the AA and Σ7 stackings. Atomistic simulations reveal that AA-vicinal systems support ferroelectricity under both small twist and small strain, with polarization switching in the latter governed by the deformation of swirling dislocations rather than the straight interface dislocations seen in the former. For Σ7-vicinal systems, where existing interatomic potentials underperform particularly under extreme out-of-plane compression, we develop a density-functional-theory-informed continuum framework—the bicrystallography-informed frame-invariant multiscale (BFIM) model, which captures out-of-plane ferroelectricity in heterodeformed configurations vicinal to Σ7 stacking. Interface dislocations in these large heterodeformed bilayer configurations exhibit markedly smaller Burgers vectors compared to interface dislocations in small-twist and small-strain bilayer hBN. The BFIM model reproduces experimental results and provides a powerful, computationally efficient framework for predicting ferroelectricity in large-unit-cell heterostructures where atomistic simulations are prohibitively expensive.
Highly alloyed (Al,Gd)N is of potential interest in a variety of applications, including neutron detection and in devices such as non-volatile memory. Gd has been shown to have very low equilibrium solubility in AlN at room temperature; however, non-equilibrium deposition techniques such as sputtering are able to deposit thin films, which incorporate large amounts of Gd. Here, we characterize a highly-alloyed (Al,Gd)N combinatorial thin film grown by RF sputtering on a GaN substrate, looking for any evidence of chemical or phase segregation or structural disorder in the films. Compositions with between 13% and 32% Gd (on a cation basis) were studied. No evidence was found for chemical or phase segregation in any studied composition. Higher degrees of Gd incorporation led to greater structural disorder in the film and a tendency toward amorphization; however, electron diffraction shows that the film does not become fully amorphous at any of the studied compositions, instead retaining textured local order even at 32% Gd. Electron energy loss spectra suggest that the material retains a locally wurtzite-like tetrahedral bonding environment at all studied compositions.
A new electrical setup for testing ECRAM devices is described in this presentation. This presentation details the background of ECRAM devices, how the B1500 is used to program ECRAM devices, and the python GUI that was created for controlling the B1500. ECRAM devices are a form of non-volatile memory. The stored data is the resistance between two of the terminals on the device. Using only the B1500, the ECRAM devices can be programmed and the state read out. This creates a simple, logical testing setup. The overall setup was successful, ECRAM devices could easily be programmed and measured. However, there is still room for improvement. Specifically, in the creation of an automated “Program” feature and a “Data Collection” feature. These features will require additional advances in the understanding of ECRAM devices but will greatly automate and improve the current data collection and application of ECRAM devices.
Persistent photoconductivity and optoelectronic synaptic behavior are demonstrated in solution-processed mesoscopic α-phase vanadium pentoxide (V 2 O 5 ) thin films. First-principles simulations coupled with the two-site Holstein polaron hopping model show that vacancies at the terminal oxygen position lead to long recombination times because photoexcited electrons and holes reside on different layers separated by the van der Waals gap, forming a weakly coupled interlayer exciton polaron. Mid-gap polaronic states also significantly broaden the photoresponse of the films to span across visible and infrared wavelengths. By controlling the amplitude/intensity, duration, and/or number of optical pulses, tunable optoelectronic memory functions, such as short-term and long-term plasticity, are experimentally established in V 2 O 5 -based optoelectronic synapses. Furthermore, device fabrication was extended to mechanically flexible ultrathin glass substrates. Flexible optoelectronic synapses maintained high performance after 150 bending cycles.
In this project, we focus on the advancement of our newly developed, unique in situ and cryogenic analytical electron microscopy (AEM) techniques and the development of additional high-end capabilities to quantitatively elucidate the mechanisms of resistive state changes in solid-state electrochemical materials for non-volatile, neuromorphic memory. The objectives of the proposed research are (1) to characterize and elucidate the mechanisms by which electronic state changes occur in various solid-state devices to build guiding principles for electrochemically activated smart materials; (2) to advance our understanding of such mechanisms by coupling cryogenic and in situ techniques to control kinetics of the observed mechanisms while mitigating probe-induced damage; and (3) to utilize additional advanced techniques including electron-beam induced current and quantum mechanical simulations in order to identify the underlying fundamental science of such processes, including roles of defect formation and its effect on phase nucleation, as well as dynamics of these processes in the electrochemical systems at the nanometer scale.
Magnons, the quanta of spin waves, have been extensively studied in a range of materials for spintronics, particularly for non-volatile logic-in-memory devices. Controlling magnons in conventional antiferromagnets and harnessing them in practical applications, however, remains a challenge. Here, we demonstrate highly efficient magnon transport in a LaFeO3/ BiFeO3/ LaFeO3 all-antiferromagnetic system, which can be controlled electrically, making it highly desirable for energy-efficient computation. Leveraging spin-orbit-driven spin-charge transduction, we demonstrate that this material architecture permits magnon confinement in ultrathin antiferromagnets, enhancing the output voltage generated by magnon transport by several orders of magnitude, which provides a pathway to enable magnetoelectric memory and logic functionalities. Additionally, the non-volatility of output voltage enables ultralowpower logic-in-memory processing, where magnonic devices can be efficiently reconfigured via electrically controlled magnon spin currents within magnetoelectric channels.