arXiv subjects

Ian Mercer

Publications and source records attributed to Ian Mercer.

7 recordsLinked to original sources

arXiv2026

Multitask Scanning Probe Microscopy

Scanning probe microscopy provides nanoscale access to structural, electrical, electromechanical, magnetic, and mechanical properties of materials. Its increasing use for wafer-scale characterization and combinatorial materials exploration creates a need to distribute measurements efficiently across large spatial domains. This is particularly important when available modalities differ in acquisition time and potential for tip and sample damage, making exhaustive multimodal mapping over spatial grids impractical. Here, we demonstrate multitask scanning probe microscopy, a live, closed-loop workflow in which a multitask Gaussian process learns spatial and cross-modal relationships and autonomously selects both the next measurement location and the next experimental protocol. The approach is implemented on an automated large-sample atomic force microscope and demonstrated on a composition-spread AlScN wafer using tapping-mode and Dual AC Resonance Tracking (DART) measurements. Paired initial measurements establish the relation between the tasks, after which noncoincident measurements are used to update both response landscapes. The resulting workflow extends active learning in scanning probe microscopy from spatial sampling to autonomous allocation of measurement modalities and provides a basis for combining rapid, weakly perturbative imaging with slower contact, electrical, electromechanical, magnetic, or spectroscopic measurements.

cond-mat.mtrl-sci↗

arXiv2026

An Automated Magnetron Sputtering Chamber for Ferroelectric Thin Film Deposition

Optimization of next-generation materials synthesis and manufacturing processes can be accelerated by effective use of digital datasets. However, a majority of existing custom research infrastructure, including that for thin film deposition, is primarily manually operated and not compatible with this new research paradigm. Here, a template is provided for upgrading existing manual deposition chambers to enable automated and autonomous experimentation. As an example, the upgrade of an existing magnetron sputtering chamber dedicated to synthesis of wurtzite ferroelectrics is presented. Focus is placed on automation of instrumentation; system and deposition control; and synchronized and automated data collection strategies. An example use case of the system for semi-autonomous determination of process-property relationships is presented, specifically minimization of coercive field in wurtzite Al$_{1-x-y}$Sc$_x$B$_y$N thin films.

cond-mat.mtrl-sci↗

arXiv2026

From Closed-Loop Optimization to Open Decision Making: Coupled Digital Twins for Predictive and Autonomous Microscopy

Automated experimentation is moving from closed-loop optimization toward open decision-making, where human or AI planners must forecast the consequences of candidate actions before executing them. Such forecasts require a model of both sides of the experiment: how the sample is likely to respond and what the instrument is likely to detect. We therefore introduce a coupled digital-twin framework that separates these roles and then links them. In this framework, the sample twin encodes material state inferred from prior knowledge and measurements till the moment. The instrument twin captures signal formation, feedback dynamics, and operating constraints based on prior knowledge. When coupled, the two twins estimate expected outcomes, uncertainty, and risk for candidate microscope operations. For amplitude-modulation scanning probe microscopy, we realize this framework with a physics-informed encoder of force-distance curves, a deterministic scanner model of cantilever and feedback dynamics, and sparse learned residual corrections. The encoder first recovers scanner-driving descriptors with sub-nanometer accuracy. The calibrated scanner then reproduces typical traces within a few nanometers and identifies operating-point noise amplification as the main source of mismatch. Supplementary phase analysis localizes residual error to the phase channel, which clarifies where added physics is needed. Together, these results establish coupled sample and instrument twins as a practical foundation for predictive microscope operation and autonomous experimental planning.

cond-mat.mtrl-sci↗

arXiv2026

Ferroelectric dynamic-field-driven nucleation and growth model for predictive materials-to-circuit co-design

Real ferroelectric devices operate under mixed and distorted time-varying voltages, yet the standard nucleation-growth frameworks used to interpret ferroelectric switching - most notably the Kolmogorov-Avrami-Ishibashi (KAI) and nucleation-limited switching models (NLS) - are derived under the critically limiting assumption of a constant electric field. Thus, the prevailing interpretation of ferroelectric switching dynamics fails under real operating conditions. Here we introduce a compact dynamic-field-driven nucleation and growth (DFNG) model that enables quantitative fits to switching transients across multiple ferroelectric materials to extract time-varying domain wall velocity and growth dimensionality, even under arbitrary voltage waveform. This capability then motivates its use in device modeling under complex signals spanning disparate time and frequency scales. Coupling the compact model to application-related waveforms and circuit-level simulation platform facilitates a predictive materials-circuit co-design framework by linking nucleation and growth parameters to memory window, disturb error, speed, and energy dissipation for next-generation ferroelectric technologies.

cond-mat.mtrl-sci↗

arXiv2025

Material-Limited Switching in Nanoscale Ferroelectrics

The ferroelectric switching speed has been experimentally obfuscated by the interaction between the measurement circuit and the ferroelectric switching itself. This has prohibited the observation of real material responses at nanosecond timescales and lower. Here, fundamental polarization switching speeds in ferroelectric materials with the perovskite, fluorite, and wurtzite structures are reported. Upon lateral scaling of island capacitors from micron to nanoscales, a clear transition from circuit-limited switching to a material-limited switching regime is observed. In La$_{0.15}$Bi$_{0.85}$FeO$_{3}$ capacitors, switching is as fast as ~150 ps, the fastest switching time reported. For polycrystalline Hf$_{0.5}$Zr$_{0.5}$O$_{2}$ capacitors, a fundamental switching limit of ~210 ps is observed. Switching times for Al$_{0.92}$B$_{0.08}$N are near 20 ns, limited by the coercive and breakdown electric fields. The activation field, instantaneous pseudo-resistivity, and energy-delay are reported in this material-limited regime. Lastly, a criterion for reaching the material-limited regime is provided. This regime enables observation of intrinsic material properties and favorable scaling trends for high-performance computing.

cond-mat.mtrl-sci↗

arXiv2025

Ferroelectric Al$_{1-x}$B$_x$N sputtered thin films on n-type Si bottom electrodes

Ferroelectric Al$_{1-x}$B$_x$N thin films are grown on highly doped and plasma treated (100) n-type Si. We demonstrate ferroelectricity for x = <0.01, 0.02, 0.06, 0.08, 0.13, and 0.17 where the n-type Si is both the substrate and bottom electrode. Polarization hysteresis reveals remanent polarization values between 130-140 $\mu$C/cm$^2$ and coercive field values as low as 4 MV/cm at 1 Hz with low leakage. The highest re-sistivity and most saturating hysteresis occurs with B contents between x = 0.06 and 0.13. We also demonstrate the impact of substrate plasma treatment time on Al$_{1-x}$B$_x$N crystallinity and switching. Cross-sectional transmission electron microscopy and electron energy loss spectra reveal an amorphous 3.5 nm SiNx layer at the Al$_{1-x}$B$_x$N interface post-plasma treatment and deposition. The first $\sim 5$ nm of Al$_{1-x}$B$_x$N is crystallographically defective. Using the n-type Si substrate we demonstrate Al$_{1-x}$B$_x$N thick-ness scaling to 25 nm via low frequency hysteresis and CV. Serving as the bottom electrode and sub-strate, the n-type Si enables a streamlined growth process for Al$_{1-x}$B$_x$N for a wide range of Al$_{1-x}$B$_x$N compositions and layer thicknesses.

cond-mat.mtrl-sci↗

arXiv2024

Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries

Combinatorial materials libraries provide a powerful platform for mapping how physical properties evolve across binary and ternary cross-sections of multicomponent phase diagrams. While synthesis of such libraries has advanced since the 1960s and been accelerated by laboratory automation, their broader utility depends on rapid, quantitative measurements of composition-dependent structures and functionalities. Scanning probe microscopies (SPM), including piezoresponse force microscopy (PFM), offer unique potential for providing these functionally relevant, spatially resolved readouts. Here, we demonstrate a fully automated SPM framework for exploring ferroelectric properties across combinatorial libraries, focusing on binary Sm-doped BiFeO3 (SmBFO) and ternary Al$_{1-x-y}$Sc$_x$B$_y$N (Al,Sc,B)N systems. In SmBFO, automated exploration identifies the known morphotropic phase boundary with enhanced ferroelectric response and reveals a previously unreported double-peak fine structure. In the (Al,Sc,B)N library, ferroelectric behavior emerges at the phase-stability boundary, correlating with variations in morphology and defect concentration. By integrating automated SPM with wavelength-dispersive spectroscopy (WDS) and photoluminescence mapping, we resolve the composition-morphology-defect-property relationships underlying ferroelectric response and demonstrate a pathway toward a multi-tool, high-throughput characterization platform. Finally, we implement Gaussian-process-based single- and multi-objective Bayesian optimization to enable autonomous exploration, highlighting the Pareto front as a powerful framework for balancing competing physical rewards and accelerating data-driven physics discovery.

cond-mat.mtrl-sci↗