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This study investigates the insertion traits of the Al2O3 and Y2O3 insertion layers (ILs) and their effects on the phase evolution and electrical characteristics of polycrystalline Hf0.5Zr0.5O2 (HZO) thin films grown by atomic layer deposition (ALD). The Al2O3 and Y2O3 ILs are located at the middle position along the HZO film. The thick Al2O3 IL, above 2–3 ALD cycles, forms a continuous layer, physically separating the upper and lower regions of the film. Conversely, the thin Al2O3 IL, below 2–3 ALD cycles, and all the Y2O3 IL diffuse into the nearby HZO layers, making a single Al- or Y-doped HZO layer. The most crucial finding is that the diffused trivalent Al and Y ions substitute the tetravalent Hf and Zr ions, creating oxygen vacancies for charge neutrality and changing the phase evolutions. The substituted Al and Y suppress the monoclinic phase and enhance the tetragonal phase. Ultimately, the study suggests a new perspective on doped HfO2-based thin films, highlighting the crucial role of substitutional diffusion of dopants and charge neutrality in determining the formation of the tetragonal phase. [ABSTRACT FROM AUTHOR]

Analog and digital switching performances in a Ta/HfO2/RuO2 (THR) memristor are studied to implement a density‐based spatial clustering of applications with noise (DBSCAN) algorithm in a low‐power, parallel‐computing memristor crossbar structure. In the analog mode THR memristor, more than 256 states can be stored through a fine‐tuning process with a denoising scheme. The analog mode crossbar array facilitates Euclidean distance calculation between any points in the given graphic dataset. In the digital mode, the on/off ratio of more than three orders of magnitude between the binary states is achieved, providing functionality to cluster the data points with a reduced number of operations. The parallel computing capacity of the adopted crossbar decreases the time complexity of the original DBSCAN from O(n2) to O(n). Through array‐level simulations, the effectiveness of hardware functionality is validated using representative synthetic datasets and single‐cell RNA sequences datasets. [ABSTRACT FROM AUTHOR]

The negative capacitance (NC) effect, recently discovered in a fluorite‐based ferroelectric thin film, has attracted great attention as a rescue to overcome the scaling limitations of the conventional memory and logic devices of highly integrated circuits. The NC effect manifesting an S‐shaped polarization–voltage (P–V) curve is initially interpreted by a 1‐dimensional Landau Ginzburg Devonshire (LGD) model. However, a series of recent studies have found that this effect can also be explained by the inhomogeneous stray field energy (ISE) model. In this study, by extending the ISE model in the ferroelectric (FE)‐dielectric (DE) layered structure, an analytical model that considers the influence of the interfacial screening charge distribution is presented. This model showed that the NC effect in the FE‐DE heterostructure can be manifested in various forms other than a single S‐shaped P–V curve. In particular, a double S‐shaped P–V curve is expected from the fully compensated anti‐parallel domain structure, confirmed experimentally in the actual Al2O3/(Hf0.5Zr0.5)O2/Al2O3 triple‐layer structure. Furthermore, to reveal the origin of the double S‐shaped P–V curve, a multidomain LGD model is presented. It is confirmed that this phenomenon is attributed to the evolution of inhomogeneous stray field energy. [ABSTRACT FROM AUTHOR]

Ferroelectric aluminum scandium nitride (Al0.7Sc0.3N) has attracted increasing interest due to its high remanent polarization (Pr, >100 µC cm−2) and coercive field (Ec, >5 MV cm−1). The four radio frequency reactive magnetron sputtering conditions (sputtering power, N2 flow ratio, pressure, and temperature) influence the ferroelectric and material properties of 45 nm‐thick Al0.7Sc0.3N deposited on the TiN/SiO2/Si substrate. Crystallinity is enhanced under the deposition conditions with higher adatom energy but deteriorates when the growth condition increases over the optimum. The well‐crystallized films have (002)‐preferred orientation with the in‐plane compressive stress imposed by the peening effect and thermal stress. The imposed compressive stress increases the c0/a0 value, where c0 and a0 mean the c‐ and a–axis lattice parameters, which eventually increases the Ec of the film. Pr increases with the c0/a0 value, but other factors also influence the change. The films with high oxygen concentration show the wake‐up properties due to the large percentage of domain walls and their depinning. Finally, ferroelectricity is confirmed with films down to a thickness of 20 nm. However, the thinnest film shows a higher Ec and lower Pr. These findings imply the presence of non‐ferroelectric interfacial layers, which induce the depolarization effect. [ABSTRACT FROM AUTHOR]

A computing scheme that can solve complex tasks is necessary as the big data field proliferates. Probabilistic computing (p-computing) paves the way to efficiently handle problems based on stochastic units called probabilistic bits (p-bits). This study proposes p-computing based on the threshold switching (TS) behavior of a Cu0.1Te0.9/HfO2/Pt (CTHP) diffusive memristor. The theoretical background of the p-computing resembling the Hopfield network structure is introduced to explain the p-computing system. P-bits are realized by the stochastic TS behavior of CTHP diffusive memristors, and they are connected to form the p-computing network. The memristor-based p-bit is likely to be '0' and '1', of which probability is controlled by an input voltage. The memristor-based p-computing enables all 16 Boolean logic operations in both forward and inverted operations, showing the possibility of expanding its uses for complex operations, such as full adder and factorization. Designing a computing scheme to solve complex tasks as the big data field proliferates remains a challenge. Here, the authors present a probabilistic bit generation hardware built using the random nature of CuxTe1−x/HfO2/Pt memristors capable of performing logic gates with invertible mode, showing the expandability to complex logic circuits. [ABSTRACT FROM AUTHOR]

Modern graph datasets with structural complexity and uncertainties due to incomplete information or data variability require advanced modeling techniques beyond conventional graph models. This study introduces a memristive crossbar array (CBA)-based probabilistic graph model (C-PGM) utilizing Cu 0.3 Te 0.7 /HfO 2 /Pt memristors, which exhibit probabilistic switching, self-rectifying, and memory characteristics. C-PGM addresses the complexities and uncertainties inherent in structural graph data across various domains, leveraging the probabilistic nature of memristors. C-PGM relies on the device-to-device variation across multiple memristive CBAs, overcoming the limitations of previous approaches that rely on sequential operations, which are slower and have a reliability concern due to repeated switching. This new approach enables the fast processing and massive implementation of probabilistic units at the expense of chip area. In this study, the hardware-based C-PGM feasibly expresses small-scale probabilistic graphs and shows minimal error in aggregate probability calculations. The probability calculation capabilities of C-PGM are applied to steady-state estimation and the PageRank algorithm, which is implemented on a simulated large-scale C-PGM. The C-PGM-based steady-state estimation and PageRank algorithm demonstrate comparable accuracy to conventional methods while significantly reducing computational costs., (© 2024 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.)

In the big data era, the requirement for data clustering methods that can handle massive and heterogeneous datasets with varying distributions increases. This study proposes a clustering algorithm for data sets with heterogeneous density using a dual-mode memristor crossbar array for data clustering. The array consists of a Ta/HfO 2 /RuO 2 memristor operating in analog or digital modes, controlled by the reset voltage. The digital mode shows low dispersion and a high resistance ratio, and the analog mode enables precise conductance tuning. The local outlier factor is introduced to handle a heterogeneous density, and the required Euclidean and K -distances within the given dataset are calculated in the analog mode in parallel. In the digital mode, clustering is performed based on the connectivity among data points after excluding the detected outliers. The proposed algorithm boasts linear time complexity for the entire process. Extensive evaluations of synthetic datasets demonstrate significant improvement over representative density-based algorithms, and the datasets with heterogeneous density are clustered feasibly. Finally, the proposed algorithm is used to cluster the single-molecule localization microscopy data, demonstrating the feasibility of the suggested method for real-world problems.

Bayesian networks and Bayesian inference, which forecast uncertain causal relationships within a stochastic framework, are used in various artificial intelligence applications. However, implementing hardware circuits for the Bayesian inference has shortcomings regarding device performance and circuit complexity. This work proposed a Bayesian network and inference circuit using a Cu 0.1 Te 0.9 /HfO 2 /Pt volatile memristor, a probabilistic bit neuron that can control the probability of being 'true' or 'false.' Nodal probabilities within the network are feasibly sampled with low errors, even with the device's cycle-to-cycle variations. Furthermore, Bayesian inference of all conditional probabilities within the network is implemented with low power (<186 nW) and energy consumption (441.4 fJ), and a normalized mean squared error of ∼7.5 × 10 -4 through division feedback logic with a variational learning rate to suppress the inherent variation of the memristor. The suggested memristor-based Bayesian network shows the potential to replace the conventional complementary metal oxide semiconductor-based Bayesian estimation method with power efficiency using a stochastic computing method., Competing Interests: The authors declare they have no conflict of interest., (This journal is © The Royal Society of Chemistry.)

Nanodevice oscillators (nano-oscillators) have received considerable attention to implement in neuromorphic computing as hardware because they can significantly improve the device integration density and energy efficiency compared to complementary metal oxide semiconductor circuit-based oscillators. This work demonstrates vertically stackable nano-oscillators using an ovonic threshold switch (OTS) for high-density neuromorphic hardware. A vertically stackable Ge 0.6 Se 0.4 OTS-oscillator (VOTS-OSC) is fabricated with a vertical crossbar array structure by growing Ge 0.6 Se 0.4 film conformally on a contact hole structure using atomic layer deposition. The VOTS-OSC can be vertically integrated onto peripheral circuits without causing thermal damage because the fabrication temperature is <400 °C. The fabricated device exhibits oscillation characteristics, which can serve as leaky integrate-and-fire neurons in spiking neural networks (SNNs) and coupled oscillators in oscillatory neural networks (ONNs). For practical applications, pattern recognition and vertex coloring are demonstrated with SNNs and ONNs, respectively, using semiempirical simulations. This structure increases the oscillator integration density significantly, enabling complex tasks with a large number of oscillators. Moreover, it can enhance the computational speed of neural networks due to its rapid switching speed.

Multiple switching modes in a Ta 2 O 5 /HfO 2 memristor are studied experimentally and numerically through a reservoir computing (RC) simulation to reveal the importance of nonlinearity and heterogeneity in the RC framework. Unlike most studies, where homogeneous reservoirs are used, heterogeneity is introduced by combining different behaviors of the memristor units. The chosen memristor for the reservoir units is based on a Ta 2 O 5 /HfO 2 bilayer, in which the conductances of the Ta 2 O 5 and HfO 2 layers are controlled by the oxygen vacancies and deep/shallow traps, respectively, providing both volatile and non-volatile resistive switching modes. These several control parameters make the second-order Ta 2 O 5 /HfO 2 memristor system present different behaviors in agreement with its history-dependent conductance and allow the fine-tuning of the behavior of each reservoir unit. The heterogeneity in the reservoir units improves the pattern recognition performance in the heterogeneous memristor RC system with a similar physical structure.