arXiv subjects

Maximilian Lederer

Publications and source records attributed to Maximilian Lederer.

7 recordsLinked to original sources

arXiv2026

Uniaxial strain-driven ferroelastic domain control in LaAlO3

Multiferroic domain walls in functional oxides exhibit properties distinct from the bulk and are increasingly exploited as active elements in nanoelectronic and photonic devices. Deterministic control of domain populations has typically remained limited to local control, or removal with temperature. Here we demonstrate continuous, reversible manipulation of the ferroelastic domain structure in single-crystal LaAlO$_3$ using in-situ uniaxial strain. Combining atomic force microscopy, X-ray diffraction, and Raman spectroscopy with first-principles calculations we map the complete microscopic evolution of the twin domain population through the strain-driven transition from the rhombohedral $R\bar{3}c$ ground state toward the predicted orthorhombic $Fmmm$ phase. Applied strains below $0.5\%$ produce pronounced surface flattening and large-scale domain reorganisation, establishing uniaxial strain as a technically accessible control parameter for ferroelastic domain engineering. These results open a route to active, real-time programming of domain architectures in LaAlO$_3$-based heterostructures, with implications for strain-tunable superconducting interfaces, nanoscale phonon-polariton optics, and ultrafast lattice control.

cond-mat.mtrl-sci↗

arXiv2025

BEOL Ferroelectric Compute-in-Memory Ising Machine for Simulated Bifurcation

Computationally hard combinatorial optimization problems are pervasive in science and engineering, yet their NP-hard nature renders them increasingly inefficient to solve on conventional von Neumann architectures as problem size grows. Ising machines implemented using dynamical, digital and compute-in-memory (CiM) approaches offer a promising alternative, but often suffer from poor initialization and a fundamental trade-off between algorithmic performance and hardware efficiency. Hardware-friendly schemes such as simulated annealing converge slowly, whereas faster algorithms, including simulated bifurcation, are difficult to implement efficiently in CiM hardware, limiting both convergence speed and solution quality. To address these limitations, here we present a ferroelectric field-effect transistor (FeFET)-based CiM Ising framework that tightly co-designs algorithms and hardware to efficiently solve large-scale combinatorial optimization problems. The proposed approach employs a two-step algorithmic flow: an attention-inspired initialization that exploits global spin topology and reduces the required iterations by up to 80%, followed by a lightweight simulated bifurcation algorithm specifically tailored for CiM implementation. To natively accelerate the core vector-matrix and vector-matrix-vector operations in both steps, we fabricate a 32x256 FeFET CiM chip using ferroelectric capacitors integrated at the back end of line of a 180-nm CMOS platform. Across Max-Cut instances with up to 100,000 nodes, the proposed hardware-software co-designed solver achieves up to a 175.9x speedup over a GPU-based simulated bifurcation implementation while consistently delivering superior solution quality.

cs.ET↗

arXiv2025

Enhanced Performance of FeFET Gate Stack via Heterogeneously co-doped Ferroelectric HfO$_2$ Films

In this work, we explore the impact of spatially controlled Zr and Al heterogeneous co-doping in HfO$_2$ thin films tailored for metal-ferroelectric-insulator-semiconductor (MFIS) gate stacks of ferroelectric field effect transistors (FeFETs). By precisely modulating the vertical arrangement of Zr and Al dopants during atomic layer deposition, we introduce deliberate compositional gradients that affect crystallization dynamics during subsequent annealing. This strategy enables us to systematically tune the phase evolution and domain nucleation within the ferroelectric layer, directly influencing device reliability and performance. From a structural perspective, our findings demonstrate that the phase composition of annealed HfO$_2$ films in MFIS stacks is primarily determined by the spatial arrangement of dopants. From an electrical perspective, we observe significant enhancement of remanent polarization and endurance of the gate stacks through heterogeneous co-doping, depending on the spatial arrangement of dopants.

cond-mat.mtrl-sci↗

arXiv2025

Revealing the Influence of Dopants on the Properties of Fluorite Structure Ferroelectrics

Fluorite structure ferroelectrics, especially hafnium oxide, are widely investigated for their application in non-volatile memories, sensors, actuators, RF devices and energy harvesters. Due to the metastable nature of the ferroelectric phase in these materials, dopants and process parameters need to be optimized for its stabilization. Here, we present clear evidence of how dopants affect the properties in this material system and solutions to achieve improved reliability, desired crystallization behavior and polarization hysteresis shape/position through co-doping. Finally, the benefits of co-doping in a variety of application fields are demonstrated.

cond-mat.mtrl-sci↗

arXiv2022

An Ultra-Compact Single FeFET Binary and Multi-Bit Associative Search Engine

Content addressable memory (CAM) is widely used in associative search tasks for its highly parallel pattern matching capability. To accommodate the increasingly complex and data-intensive pattern matching tasks, it is critical to keep improving the CAM density to enhance the performance and area efficiency. In this work, we demonstrate: i) a novel ultra-compact 1FeFET CAM design that enables parallel associative search and in-memory hamming distance calculation; ii) a multi-bit CAM for exact search using the same CAM cell; iii) compact device designs that integrate the series resistor current limiter into the intrinsic FeFET structure to turn the 1FeFET1R into an effective 1FeFET cell; iv) a successful 2-step search operation and a sufficient sensing margin of the proposed binary and multi-bit 1FeFET1R CAM array with sizes of practical interests in both experiments and simulations, given the existing unoptimized FeFET device variation; v) 89.9x speedup and 66.5x energy efficiency improvement over the state-of-the art alignment tools on GPU in accelerating genome pattern matching applications through the hyperdimensional computing paradigm.

cs.ET↗

arXiv2022

Megahertz-rate Ultrafast X-ray Scattering and Holographic Imaging at the European XFEL

The advent of X-ray free-electron lasers (XFELs) has revolutionized fundamental science, from atomic to condensed matter physics, from chemistry to biology, giving researchers access to X-rays with unprecedented brightness, coherence, and pulse duration. All XFEL facilities built until recently provided X-ray pulses at a relatively low repetition rate, with limited data statistics. Here, we present the results from the first megahertz repetition rate X-ray scattering experiments at the Spectroscopy and Coherent Scattering (SCS) instrument of the European XFEL. We illustrate the experimental capabilities that the SCS instrument offers, resulting from the operation at MHz repetition rates and the availability of the novel DSSC 2D imaging detector. Time-resolved magnetic X-ray scattering and holographic imaging experiments in solid state samples were chosen as representative, providing an ideal test-bed for operation at megahertz rates. Our results are relevant and applicable to any other non-destructive XFEL experiments in the soft X-ray range.

cond-mat.mes-hall↗

arXiv2021

Deep Random Forest with Ferroelectric Analog Content Addressable Memory

Deep random forest (DRF), which incorporates the core features of deep learning and random forest (RF), exhibits comparable classification accuracy, interpretability, and low memory and computational overhead when compared with deep neural networks (DNNs) in various information processing tasks for edge intelligence. However, the development of efficient hardware to accelerate DRF is lagging behind its DNN counterparts. The key for hardware acceleration of DRF lies in efficiently realizing the branch-split operation at decision nodes when traversing a decision tree. In this work, we propose to implement DRF through simple associative searches realized with ferroelectric analog content addressable memory (ACAM). Utilizing only two ferroelectric field effect transistors (FeFETs), the ultra-compact ACAM cell can perform a branch-split operation with an energy-efficient associative search by storing the decision boundaries as the analog polarization states in an FeFET. The DRF accelerator architecture and the corresponding mapping of the DRF model to the ACAM arrays are presented. The functionality, characteristics, and scalability of the FeFET ACAM based DRF and its robustness against FeFET device non-idealities are validated both in experiments and simulations. Evaluation results show that the FeFET ACAM DRF accelerator exhibits 10^6x/16x and 10^6x/2.5x improvements in terms of energy and latency when compared with other deep random forest hardware implementations on the state-of-the-art CPU/ReRAM, respectively.

cs.ET↗