Nico Kaiser

Efficient computing systems require low‐power and high‐density memory technologies capable of supporting advanced applications such as neuromorphic computing and artificial intelligence. Hafnium oxide (HfO ₂ ) based resistive random‐access memory (RRAM) is a promising candidate to this end because of its Complementary Metal‐Oxide‐Semiconductor (CMOS) compatibility and reliable switching behavior. In this work, the effects of lanthanum (La) doping in hafnium oxide are assessed in connection with the memristor performance for neuromorphic applications. It shows that substitution of La stabilizes the cubic phase of (Hf,La)O ₂ lowering the oxygen vacancies formation energy and, thereby, favoring their migration properties. La‐doped HfO ₂ ‐based memristors show reduced forming and operating voltages, improved switching uniformity, and increased endurance. These memristors exhibit electric field‐driven resistive switching with low voltage variability and most important linear conductance modulation under ultra‐fast pulsed stimulation. These improvements indicate that La‐doped HfO ₂ memristors offer significant advantages for energy efficient and reliable analog computing as compared to undoped devices. These findings suggest that La‐doped HfO ₂ RRAM has the potential to be a key technology for future analog neuromorphic applications in next‐generation computing architectures.

Resistive random-access memory based on hafnia is a potential candidate for next-generation memories. However, there are some key challenges to overcome, including the stochastic nature of the filament formation during electroforming and SET operation, which results in large cycle-to-cycle and device-to-device variability. Here, we propose a device setup with an artificial virtual electrode layer of conducting oxygen-deficient HfO_x and a well-defined self-limited oxidized switching layer to reduce the stochasticity of filament growth. We have realized this concept in TiN/HfO_x/HfO₂/Pt device stacks by adjusting the oxygen content in the artificial virtual electrode during growth in a molecular beam epitaxy system and subsequent self-limited surface oxidation in an oxygen-rich atmosphere. The artificial virtual electrode functions as a conducting oxygen vacancy exchange layer (COVEL) due to the inherent low oxygen content, providing an unlimited oxygen vacancy reservoir that stabilizes the filament in the thin oxidized switching layer. Detailed electrical characterization demonstrates that the COVEL, combined with the self-limited oxidized switching layer, can significantly reduce electroforming and SET voltage stochasticity and thus contribute to improving the switching reliability.

We explored the role of electric-field engineering in controlling filamentary-type conduction in Cu/HfO2/Pt memristive devices, utilizing the quantum point-contact (QPC) model for analysis. In a previous study, we reported that devices with a thicker oxide layer stack (20 and 15 nm) statistically exhibit higher resistance values in the high-resistance state (HRS) compared to devices with a thinner oxide layer stack (10 nm). In addition, thicker oxides exhibit higher and more broadly distributed set voltages compared to the more uniform distribution of set voltages occurring in thinner oxide layers. In this work, we thoroughly analyzed the electron transport characteristics of the electroformed devices (Cu conducting filament) using the QPC model, which allows us to infer the main features of the confinement potential barrier at the constriction's bottleneck. The obtained results indicate that the HRS current for the three thicknesses investigated follows a trend consistent with the predictions of the QPC model in terms of the aspect ratio energy barrier thickness/constriction radius (tB/R). Notably, the 10 nm samples exhibit a lower aspect ratio (tB/R < 1: wide nanogap) than the 15 and 20 nm samples (tB/R > 1: elongated nanogap). It was found that wide nanogaps are associated with lower variance of the HRS current and smaller set voltages. In view of these results, we suggest that optimizing the switching oxide layer thickness, therefore the shape of the filament, may be a suitable way to reduce variability of HRS current and set voltage to improve resistive switching performance.

Transition metal oxide dielectric layers have emerged as promising candidates for various relevant applications, such as supercapacitors or memory applications. However, the performance and reliability of these devices can critically depend on their microstructure, which can be strongly influenced by thermal processing and substrate-induced strain. To gain a more in-depth understanding of the microstructural changes, we conducted in situ transmission electron microscopy (TEM) studies of amorphous HfO₂ dielectric layers grown on highly textured (111) substrates. Our results indicate that the minimum required phase transition temperature is 180 °C and that the developed crystallinity is affected by texture transfer. Using in situ TEM and 4D-STEM can provide valuable insights into the fundamental mechanisms underlying the microstructural evolution of dielectric layers and could pave the way for the development of more reliable and efficient devices for future applications.

HfO₂ is one of the most common memristive materials and it is widely accepted that oxygen vacancies are prerequisite to reduce the forming voltage of the respective memristive devices. Here, a series of six oxygen engineered substoichiometric HfO_{2 − x} thin films with varying oxygen deficiency is investigated by conductive atomic force microscopy (c‐AFM) and the switching process of substoichiometric films is observed on the nanoscale. X‐ray diffractometry (XRD) exhibits a phase transition from stoichiometric, monoclinic HfO₂ toward oxygen deficient, rhombohedral HfO_1.7. The conductance of HfO_{2 − x} is increasing with increasing oxygen deficiency, which is consistent with the increasing prevalence of the highly conductive rhombohedral phase. Simultaneously, c‐AFM reveals significant local conductivity differences between grains and grain boundaries, regardless of the level of oxygen deficiency. Single grains of highly oxygen deficient samples are formed at significant lower voltages. The mean forming voltage is reduced from (7.0 ± 0.6) V for HfO₂ to (1.9 ± 0.8) V for HfO_1.7. Resistive switching on the nanoscale is established for single grains for the highest deficient thin film samples. The final resistance state is thereby dependent on the initial conductivity of the grains. These studies offer valuable insights into the switching behavior of memristive polycrystalline HfO₂.

This paper reports a simulation study concerning the effect of yttrium oxide stoichiometry on output features of a memristor-based single layer perceptron neural network. To carry out this investigation, a material-oriented behavioral compact model for bipolar-type memristive devices was developed and tested. The model is written for the SPICE (Simulation Program with Integrated Circuits Emphasis) simulator and considers as one of its inputs a measure of the oxygen flow used during the deposition of the switching layer. After a thorough statistical calibration of the model parameters using experimental current–voltage characteristics associated with different fabrication conditions, the corresponding curves were simulated and the results were compared with the original data. In this way, the average switching behavior of the structures (low and high current states, set and reset voltages, etc.) as a function of the oxygen content can be forecasted. In a subsequent phase, the collective response of the devices when used in a neural network was investigated in terms of the output features of the network (mainly power dissipation and power efficiency). The role played by parasitic elements, such as the line resistance and the read voltage influence on the inference accuracy, was also explored. Since a similar strategy can be applied to any other material-related fabrication parameter, the proposed approach opens up a new dimension for circuit designers, as the behavior of complex circuits employing devices with specific characteristics can be realistically assessed before fabrication.

Hafnium oxide is an outstanding candidate for next-generation nonvolatile memory solutions such as OxRAM (oxide-based resistive memory) and FeRAM (ferroelectric random access memory). A key parameter for OxRAM is the controlled oxygen deficiency in HfO_2-x which eventually is associated with structural changes. Here, we expand the view on the recently identified (semi-)conducting low-temperature pseudocubic phase of reduced hafnium oxide by further X-ray diffraction analysis and density functional theory (DFT) simulation and reveal its rhombohedral nature. By performing total energy and electronic structure calculations, we investigate phase stability and band structure modifications in the presence of oxygen vacancies. With increasing oxygen vacancy concentration, the material transforms from the well-known monoclinic structure to a (pseudocubic) polar rhombohedral r-HfO_2-x structure. The DFT analysis shows that r-HfO_2-x is not merely epitaxy-induced but may exist as a relaxed compound. Furthermore, the electronic structure of r-HfO_2-x as determined by X-ray photoelectron spectroscopy and UV/Vis spectroscopy corresponds very well with the DFT-based prediction of a conducting defect band. The existence of a substoichiometric (semi-)conducting phase of HfO_2-x is obviously an important ingredient to understand the mechanism of resistive switching in hafnium-oxide-based OxRAM.

HfO₂-based resistive random-access memory devices are promising candidates for new memory and computing applications. Hereby, scaling of the devices is a key issue, where overall fundamental switching and conduction mechanisms can be easily influenced by changes in the oxide layer thickness. This work addresses the oxide thickness-dependent resistive switching characteristics in Cu/HfO₂/Pt memory devices through bipolar DC switching characterization. Forming, reset, and set characteristics are investigated depending on the oxide layer thickness, revealing a significant difference for thicker compared to thinner films. Thicker samples tend to show a more abrupt reset behavior and a larger set voltage variance, while for thinner samples, a more gradual reset behavior and a low set variance is found. These phenomena can be explained by a model based on thermally assisted electrochemical metallization. Furthermore, to understand the conduction mechanism of the devices, current–voltage curves of the set process were investigated. The devices are found to have an Ohmic conduction mechanism in the lower voltage region generally, while thinner samples tend to show an additional space-charge-limited current conduction mechanism in a higher voltage region.

In this paper, the use of Artificial Neural Networks (ANNs) in the form of Convolutional Neural Networks (AlexNET) for the fast and energy-efficient fitting of the Dynamic Memdiode Model (DMM) to the conduction characteristics of bipolar-type resistive switching (RS) devices is investigated. Despite an initial computationally intensive training phase the ANNs allow obtaining a mapping between the experimental Current-Voltage (I-V) curve and the corresponding DMM parameters without incurring a costly iterative process as typically considered in error minimization-based optimization algorithms. In order to demonstrate the fitting capabilities of the proposed approach, a complete set of I-Vs obtained from Y₂O₃-based RRAM devices, fabricated with different oxidation conditions and measured with different current compliances, is considered. In this way, in addition to the intrinsic RS variability, extrinsic variation is achieved by means of external factors (oxygen content and damage control during the set process). We show that the reported method provides a significant reduction of the fitting time (one order of magnitude), especially in the case of large data sets. This issue is crucial when the extraction of the model parameters and their statistical characterization are required.

Hafnium oxide- and GeSbTe-based functional layers are promising candidates in material systems for emerging memory technologies. They are also discussed as contenders for radiation-harsh environment applications. Testing the resilience against ion radiation is of high importance to identify materials that are feasible for future applications of emerging memory technologies like oxide-based, ferroelectric, and phase-change random-access memory. Induced changes of the crystalline and microscopic structure have to be considered as they are directly related to the memory states and failure mechanisms of the emerging memory technologies. Therefore, we present heavy ion irradiation-induced effects in emerging memories based on different memory materials, in particular, HfO₂-, HfZrO₂-, as well as GeSbTe-based thin films. This study reveals that the initial crystallinity, composition, and microstructure of the memory materials have a fundamental influence on their interaction with Au swift heavy ions. With this, we provide a test protocol for irradiation experiments of hafnium oxide- and GeSbTe-based emerging memories, combining structural investigations by X-ray diffraction on a macroscopic, scanning transmission electron microscopy on a microscopic scale, and electrical characterization of real devices. Such fundamental studies can be also of importance for future applications, considering the transition of digital to analog memories with a multitude of resistance states.