Negative differential resistance (NDR) behavior observed in several transition metal oxides is crucial for developing next-generation memory devices and neuromorphic computing systems. NbO2-based memristors exhibit two regions of NDR at room temperature, making them promising candidates for such applications. Despite this potential, the physical mechanisms behind the onset and the ability to engineer these NDR regions remain unclear, hindering further development of these devices for applications. This study employed electrical transport and ultra-low frequency noise spectroscopy measurements to investigate two distinct NDR phenomena in nanoscale thin films of NbO2. By analyzing the residual current fluctuations as a function of time, spatially inhomogeneous and non-linear conduction are found near NDR-1 and a two-state switching near NDR-2, leading to an insulator-to-metal (IMT) transition. The power spectral density of the residual fluctuations exhibits significantly elevated noise magnitudes around both NDR regions, providing insights into physical mechanisms and device size scaling for electronic applications. A simple theoretical model, based on the dimerization of correlated insulators, offers a comprehensive explanation of observed transport and noise behaviors near NDRs, affirming the presence of non-linear conduction followed by an IMT connecting macroscopic device response to transport signatures at the atomic level.

Phase change memory (PCM) is considered an enabling technology for non-volatile multilevel data storage and neuromorphic computing. Recent advancements in PCM have highlighted the need to improve resistance drift and energy efficiency. At present, binary alloys that phase-separate upon crystallization offer a promising solution. The Al–Sb binary alloy crystallizes into a rhombohedral Sb-rich phase and a cubic AlSb phase, with the latter having a higher melting temperature that enables selective melting of the Sb-rich phase for partial RESET programming. Continuum resistance states result from a reversible alloying process, in which programming pulses modulate the granularity and aluminum content of the amorphous Sb-rich phase. Al0.4–Sb0.6 PCM cells, fabricated on Si-foundry templates, exhibit a high resistance contrast of up to 4000× between fully amorphous and crystalline states, along with a low resistance drift coefficient (∼0.06). The high melting point of AlSb also leads to nanoscale compositional heterogeneity, which persists in the amorphous state, suppressing structural relaxation and thus reducing resistance drift. These findings position Al0.4–Sb0.6 as a promising material for engineering multilevel PCM cells based on phase-separating alloys.

Resistive Random Access Memory (ReRAM) is a novel non-volatile memory technology, with potential applications spanning high-density memory and embedded memory in various non-von Neumann computing architectures. This study investigated the dependency of ReRAM switching parameters on the stoichiometry of the tantalum oxide switching layer. Devices were fabricated using reactive sputtering where oxygen partial pressure was varied during deposition of the switching layer. X-ray photoelectron spectroscopy was employed to evaluate the resulting tantalum oxide film composition, showing distinct Ta sub-oxides for each oxygen partial pressure implemented during reactive sputtering. Electrical characterization revealed optimal device performance, with sub-3 V forming voltage and memory window >10 for ReRAM devices deposited with 0.14 mTorr pO2. Devices fabricated at lower pO2 and excessively high pO2 failed to exhibit resistive switching behavior.

Artificial-intelligence-powered edge devices are inspiring interest in always-on, intelligent, and self-powered visual perception systems. Due to the high energy cost of converting raw data and the limited computing and energy resources available, designing energy-efficient and low bandwidth CMOS vision sensors is vital as these emerging systems require continuous sensing and instant processing. This paper proposes a low-power integrated sensing and computing engine, namely APRIS, including a novel software/hardware co-design technique. This method provides a highly parallel analog multiplication and accumulation-in-pixel scheme, which realizes low-precision quantized weight neural networks to mitigate the overhead of analog-to-digital converters and analog buffers. Moreover, in order to reduce the size and power consumption, we propose the implementation of an approximate ADC in the readout circuit. Our system utilizes eight memory banks to increase computation parallelism, which has a dramatic effect on its speed and efficiency. Moreover, the proposed structure supports a zero-skipping scheme to reduce power consumption further. Our circuit-to-application co-simulation results demonstrate a comparable accuracy for our platform to the full-precision baseline on various object classification tasks while reaching an efficiency of similar to 3.48 TOp/s/W.

The demand for differentiated devices in the back-end-of-line increases necessitates the need for a test vehicle that enables the integration and electrical characterization of such devices. An Insulator-Metal Transition based NbO2 device was integrated onto the NY CREATES/UAlbany memory test vehicle (MTV) utilizing a 65nm process technology to fabricate nanoscale devices with a footprint down to 120x120 nm(2). NbO2, the metastable allotrope of niobium oxide, was deposited onto coupons diced out of the 300mm MTV. This occurred in an oxygen-controlled environment yielding phase-pure NbO2 after a 750 degrees C crystallization anneal. An endurance of at least 25x10(6) cycles was demonstrated with an R-off/R-on ratio above 100 and an extrapolation towards >2000 with further device scaling.

Significant research has focused on low-power stochastic devices built from memristive materials. These devices foster neuromorphic approaches to computational efficiency enhancement in merged biomimetic and CMOS architectures due to their ability to phase transition from a dielectric to a metal at an increased temperature. Niobium dioxide has a volatile memristive phase change that occurs ∼800^∘C that makes it an ideal candidate for future neuromorphic electronics. A straightforward optical system has been developed on a horizontal tube furnace for in situ spectral measurements as an as-grown film is annealed and ultimately crystallizes as . The system measures the changing spectral transmissivity of as it undergoes both reduction and crystallization processes. We were also able to measure the transition from metallic-to-non-metallic during the cooldown phase, which is shown to occur about 100^∘C lower on a sapphire substrate than fused silica. After annealing, the material properties of the and were assessed via X-ray photoelectron spectroscopy, X-ray diffraction, and 4-point resistivity, confirming that we have made crystalline .

The oxygen diffusion rate in hafnia (HfO2)-based resistive memory plays a pivotal role in enabling nonvolatile data retention. However, the information retention times obtained in HfO2 resistive memory devices are many times higher than the expected values obtained from oxygen diffusion measurements in HfO2 materials. In this study, we resolve this discrepancy by conducting oxygen isotope tracer diffusion measurements in amorphous hafnia (a-HfO2) thin films. Our results show that the oxygen tracer diffusion in amorphous HfO2 films is orders of magnitude lower than that of previous measurements on monoclinic hafnia (m-HfO2) pellets. Moreover, oxygen tracer diffusion is much lower in denser a-HfO2 films deposited by atomic layer deposition (ALD) than in less dense a-HfO2 films deposited by sputtering. The ALD films yield similar oxygen diffusion times as experimentally measured device retention times, reconciling this discrepancy between oxygen diffusion and retention time measurements. More broadly, our work shows how processing conditions can be used to control oxygen transport characteristics in amorphous materials without long-range crystal order. We quantified the oxygen tracer diffusion in amorphous hafnium oxide thin films. These tracer diffusion values are consistent with the experimentally measured retention times of hafnium oxide resistive memory devices.

This work presents the first resistive random access memory (RRAM)-based compute-in-memory (CIM) macro design tailored for genome processing. We analyze and demonstrate two key types of genome processing applications using our developed CIM chip prototype: the state-of-the-art (SOTA) burrows–wheeler transform (BWT)-based DNA short-read alignment and alignment-free mRNA quantification. Our CIM macro is designed and optimized to support the major functions essential to these algorithms, e.g., parallel xnor operations, count, addition, and parallel bit-wise and operations. The proposed CIM macro prototype is fabricated with monolithic integration of HfO $_2$ RRAM and 65-nm CMOS, achieving 2.07 TOPS/W (tera-operations per second per watt) and 2.12 G suffixes/J (suffixes per joule) at 1.0 V, which is the most energy-efficient solution to date for genome processing.

Electronic switches based on the migration of high-density point defects, or memristors, are poised to revolutionize post-digital electronics. Despite significant research, key mechanisms for filament formation and oxygen transport remain unresolved, hindering our ability to predict and design device properties. For example, experiments have achieved 10 orders of magnitude longer retention times than predicted by current models. Here, using electrical measurements, scanning probe microscopy, and first-principles calculations on tantalum oxide memristors, we reveal that the formation and stability of conductive filaments crucially depend on the thermodynamic stability of the amorphous oxygen-rich and oxygen- poor compounds, which undergo composition phase separation. Including the previously neglected effects of this amorphous phase separation reconciles unexplained discrepancies in retention and enables predictive design of key performance indicators such as retention stability. This result emphasizes non-ideal thermodynamic interactions as key design criteria in post-digital devices with defect densities substantially exceeding those of today's covalent semiconductors.

The electrical properties and performance characteristics of niobium dioxide (NbO_2)-based memristive devices are examined at cryogenic temperatures. Sub-stoichiometric Nb_2O_5 was deposited via magnetron sputtering and patterned in microscale (2×2 - 15×15 μm^2) cross-bar Au/Ru/NbO_x/Pt devices and electroformed at 3-5 V to make NbO_2 filaments. At cryogenic temperatures, the threshold voltage (V_th) increased by more than a factor of 3. The hold voltage (V_h) was significantly lower than the threshold voltage for fast voltage sweeps (200 ms per measurement). If the sample is allowed to cool between voltage measurements, the hold voltage increases, but never reaches the threshold voltage, indicating the presence of non-volatile Nb_2O_5 in the filament. The devices have an activation energy of E_a ≈ 1.4 eV, lower than other NbO_2 devices reported. Our works shows that even nominally “bad" memristive devices can be improved by reducing the leakage current and increases the sample resistance at cryogenic temperatures.

Resistive Random-Access Memory (RRAM) presents a transformative technology for diverse computing and artificial intelligence applications. However, variability in the high resistance state (HRS) has proved to be a challenge, impeding its widespread adoption. This study focuses on optimizing TaOx-based RRAMs by strategically placing a nitrogen-doped TaOx barrier-layer (BL) to mitigate variability in the HRS. Through comprehensive electrical characterization and measurements, we uncover the critical influence of BL positioning on HRS variability and identify the optimal location of the BL to achieve a 2x lowering of HRS variability as well as an expanded range of operating voltages. Incremental reset pulse amplitude measurements show that the TaOx:N maintains a low HRS variability even at higher operating voltages when the position of the BL is optimized. Our findings offer insights into stable and reliable RRAM operation, highlighting the potential of the proposed BL to enhance the functionality of TaOx-based RRAMs and elevate overall device performance.

Niobium dioxide has a volatile memristive phase change that occurs ∼800 °C that makes it an ideal candidate for future neuromorphic electronics. A straightforward optical system has been developed on a horizontal tube furnace for in situ spectral measurements as an as-grown Nb2O5 film is annealed and ultimately crystallizes as NbO2. The system measures the changing spectral transmissivity of Nb2O5 as it undergoes both reduction and crystallization processes. We were also able to measure the transition from metallic-to-non-metallic NbO2 during the cooldown phase, which is shown to occur about 100 °C lower on a sapphire substrate than fused silica. After annealing, the material properties of the Nb2O5 and NbO2 were assessed via x-ray photoelectron spectroscopy, x-ray diffraction, and 4-point resistivity, confirming that we have made crystalline NbO2.

The electrical properties and performance characteristics of niobium dioxide (NbO2)-based memristive devices are examined at cryogenic temperatures. Sub-stoichiometric Nb2O5 was deposited via magnetron sputtering and patterned in microscale (2x2 - 15x15 mu m(2)) cross-bar Au/Ru/NbOx/Pt devices and electroformed at 3-5 V to make NbO2 filaments. At cryogenic temperatures, the threshold voltage (V-th) increased by more than a factor of 3. The hold voltage (Vh) was significantly lower than the threshold voltage for fast voltage sweeps (200 ms per measurement). If the sample is allowed to cool between voltage measurements, the hold voltage increases, but never reaches the threshold voltage, indicating the presence of non-volatile Nb2O5 in the filament. The devices have an activation energy of E-a approximate to 1.4 eV, lower than other NbO2 devices reported. Our works shows that even nominally ``bad" memristive devices can be improved by reducing the leakage current and increases the sample resistance at cryogenic temperatures.

Photonic technologies promise to deliver quantitative, multiplex, and inexpensive medical diagnostic platforms by leveraging the highly scalable processes developed for the fabrication of semiconductor microchips. However, in practice, the affordability of these platforms is limited by complex and expensive sample handling and optical alignment. We previously reported the development of a disposable photonic assay that incorporates inexpensive plastic micropillar microfluidic cards for sample delivery. That system as developed was limited to singleplex assays due to its optical configuration. To enable multiplexing, we report a new approach addressing multiplex light I/O, in which the outputs of individual grating couplers on a photonic chip are mapped to fibers in a fiber bundle. As demonstrated in the context of detecting antibody responses to influenza and SARS-CoV-2 antigens in human serum and saliva, this enables multiplexing in an inexpensive, disposable, and compact format.

Memristors, non-volatile switching memory platform, has recently attracted significant interest, offering unique potential to enable the realization of human brain-like neuromorphic computing efficiency. Memristors also demonstrate excellent temperature tolerance, long-term durability, and high tunability with nanosecond pulses, making them highly attractive for neuromorphic computing applications. To better understand the material processing, microstructure, and property relationship of switching mechanisms in memristor devices, computational methodologies, and tools are developed to predict the I-V characteristics of memristor devices based on tantalum oxide (TaOx) resistive random-access memory (ReRAM) integrated with an n-channel metal-oxide-semiconductor (NMOS) transistor. A multiphysics model based on coupled partial differential equations for electrical and thermal transport phenomena is solved for the high- and low-resistance states during the formation, growth, and destruction of a conducting filament through SET and RESET stages. These stages effectively represent the migration of oxygen vacancies within an oxide exchange layer. A series of parametric studies and energy minimization calculations are conducted to determine probable ranges for key material and model parameters accounting for the experimental data. The computational model successfully predicted the measured I-V curves across various gate voltages applied to the NMOS transistor in the one transistor one resistance (1T1R) configuration.

In this work, hafnium zirconium oxide (HZO)-based 100 × 100 nm2 ferroelectric tunnel junction (FTJ) devices were implemented on a 300 mm wafer platform, using a baseline 65 nm CMOS process technology. FTJs consisting of TiN/HZO/TiN were integrated in between metal 1 (M1) and via 1 (V1) layers. Cross-sectional transmission electron microscopy and energy dispersive x-ray spectroscopy analysis confirmed the targeted thickness and composition of the FTJ film stack, while grazing incidence, in-plane x-ray diffraction analysis demonstrated the presence of orthorhombic phase Pca21 responsible for ferroelectric polarization observed in HZO films. Current measurement, as a function of voltage for both up- and down-polarization states, yielded a tunneling electroresistance (TER) ratio of 2.28. The device TER ratio and endurance behavior were further optimized by insertion of thin Al2O3 tunnel barrier layer between the bottom electrode (TiN) and ferroelectric switching layer (HZO) by tuning the band offset between HZO and TiN, facilitating on-state tunneling conduction and creating an additional barrier layer in off-state current conduction path. Investigation of current transport mechanism showed that the current in these FTJ devices is dominated by direct tunneling at low electric field (E < 0.4 MV/cm) and by Fowler–Nordheim (F–N) tunneling at high electric field (E > 0.4 MV/cm). The modified FTJ device stack (TiN/Al2O3/HZO/TiN) demonstrated an enhanced TER ratio of ∼5 (2.2× improvement) and endurance up to 106 switching cycles. Write voltage and pulse width dependent trade-off characteristics between TER ratio and maximum endurance cycles (Nc) were established that enabled optimal balance of FTJ switching metrics. The FTJ memory cells also showed multi-level-cell characteristics, i.e., 2 bits/cell storage capability. Based on full 300 mm wafer statistics, a switching yield of >80% was achieved for fabricated FTJ devices demonstrating robustness of fabrication and programming approach used for FTJ performance optimization. The realization of CMOS-compatible nanoscale FTJ devices on 300 mm wafer platform demonstrates the promising potential of high-volume large-scale industrial implementation of FTJ devices for various nonvolatile memory applications.

Vector math operations are considered one of the basic operations for computationally intensive algorithms. The latest emerging resistive random access memory device (ReRAMI) and ReRAM crossbar arrays have shown convincing results for analog vector matrix multiplication with high energy efficiency and hence, are of great interest for computing applications because of their capability to perform array level in-memory computing in a single step. To perform fast and accurate vector math operations to a column of 1-transistor 1ReRAM (ITIR) devices in an array, a hardware interface that provides a precise control over programming the devices is needed. In this work, we demonstrate a microcontroller-based custom printed circuit board (PCB) design to perform device level testing and in-memory computation on packaged hafnium oxide based ITIR ReRAM arrays fabricated at SUNY Polytechnic Institute. Experimental results demonstrate that when ITIR ReR.AM array devices are programmed as logic states “0” high resistance state (HRS) and “1” low resistance state (LRS), the observed current ($I_{\text {sut}}$) shows the effect of read variability based on the number and position of the devices that are read. Overlapping of the current outputs can be reduced by using write-verify while programming the array of devices.

Tellurium-free antimony-based phase change memory (PCM) alloys have attracted much attention due to their superior attributes such as fast switching speed, wide resistance window, and low drift. However, programming reproducible intermediate states in such PCM materials has been challenging. In this study, bilayer PCM cells comprised of Ga–Sb films with two different compositions separated by a 1-nm-thick AlO x diffusion barrier layer were fabricated on Si foundry templates with a Ø120-nm TiN heater and TaN top contact. The current–voltage measurements of the cells exhibit two threshold voltages, separating three stable resistance regions. These cells can be controllably switched among three resistance states, that is, the SET ( $\sim 10^{3} \ \Omega)$ , intermediate ( $\sim 10^{4} \ \Omega)$ , and RESET states ( $\sim 5\times 10^{5}\,\,\ \Omega {)}$ . The phase transitions during switching among three resistance states are discussed and correlated with the device resistance profiles. The stability of the AlO x barrier layer is investigated with transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS).

In-memory computing is an emerging computing paradigm that sidesteps challenges inherent to deep learning acceleration in conventional systems. Along with the development of neuromorphic architectures, resistive randomaccess memory (RRAM) has paved the way for in-memory computing by processing mixed-signal operations in a fully parallel fashion. In this work, we designed and implemented working prototypes of in-memory operators using a custom 65nm CMOS/RRAM technology node fabricated on a 300mm wafer. Specifically, arrays of hafnium-oxide RRAM cells were built in a crossbar structure to support high-throughput matrix multiplications at low energy and area consumption. Building upon these efficient RRAM, applications of pixel detection and flow-based Boolean operations are presented. Our introduced approaches alleviate the intermediate data movement and parallelize the computations, thereby yielding orders of magnitude improvement in energy and area efficiency over the equivalent CMOS design.

In genomic analysis, the major computation bottleneck is the memory-and compute-intensive DNA short reads alignment due to memory-wall challenge. This work presents the first Resistive RAM (RRAM) based Compute-in-Memory (CIM) macro design for accelerating state-of-the-art BWT based genome sequencing alignment. Our design could support all the core instructions, i.e., XNOR based match, count, and addition, required by alignment algorithm. The proposed CIM macro implemented in integration of HfO 2 RRAM and 65nm CMOS demonstrates the best energy efficiency to date with 2.07 TOPS/W and 2. 12Gsuffixes/J at 1. 0V.