arXiv subjects

Hyunmin Cho

Publications and source records attributed to Hyunmin Cho.

10 recordsLinked to original sources

arXiv2026

Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation

We study sinusoidal recurrence as an iterative mechanism for harmonic spectral enrichment in implicit neural representations (INRs). Our analysis reveals that sinusoidal activations induce a harmonic line spectrum, providing a spectral account of how recurrent unrolling enriches the effective spectral support. We realize this principle with a shared sinusoidal block that iteratively refines the latent representation. We empirically validate the resulting spectral behavior against feed-forward INRs, non-sinusoidal recurrent variants, and equilibrium-style sinusoidal models. Complementing this analysis, we evaluate the proposed architecture across image and 3D representation tasks. On RGB image benchmarks, our method achieves higher fidelity than feed-forward baselines with fewer parameters and fewer optimization steps, and it further transfers favorably to super-resolution, NeRF, and SDF tasks.

cs.CV↗

arXiv2026

Compositional gradient engineering for enhanced ferroelectricity in ultrathin AlScN

Ferroelectric AlScN is promising for CMOS-compatible non-volatile memory, but thickness scaling is limited by leakage, premature breakdown, and defect-mediated failure. Here we show that compositional grading within a continuous wurtzite AlN-AlScN lattice mitigates these limitations by distributing structural and polarization discontinuities across the film thickness, reducing defect formation and local field concentration. In a 20 nm graded heterostructure, monotonic Sc incorporation and AlN-rich boundaries produce reversible ferroelectric switching, an as-grown metal-polar state, a 21% higher breakdown field, 10% enhanced remanent polarization, and 40x higher resistivity relative to homogeneous AlScN. Time-domain PUND measurements reveal strongly suppressed post-switching leakage, consistent with reduced defect-assisted and polarization-coupled conduction. This improved dielectric robustness enables ferroelectric functionality in 5 nm graded stacks containing only a 2 nm $\mathrm{Al}_{0.64}\mathrm{Sc}_{0.36}\mathrm{N}$ region, with measurable switching near 1 V. These results establish compositional grading as a defect- and field-management strategy for scalable ultrathin wurtzite ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2026

Balancing Fidelity and Diversity in Diffusion Models via Symmetric Attention Decomposition: Hopfield Perspective

We characterize the pre-softmax attention matrix $\mathbf{QK^\top}$ in transformers as an associative memory matrix encoding pairwise associations between input features. By decomposing this matrix into its symmetric and skew-symmetric parts, we interpret the symmetric component as governing the structure of the energy landscape, and the skew-symmetric component as driving circulation on that landscape. Leveraging the energy formulation induced by the symmetric component, we derive Hopfield-style stability measures that quantify the stability of retrieved features. We observe meaningful correlations between Hopfield-style stability measures and the fidelity-diversity trade-offs in generation. Finally, we propose a controllable knob to modulate this trade-off by modifying the circulation of the underlying dynamics. Code is available at our GitHub (https://github.com/hyeon-cho/Attention-Symmetric-Decomposition).

cs.LG↗

arXiv2026

Controlled non-volatile modulation of optical dispersion in monolayer tungsten disulfide via ferroelectric polarization patterning

The manipulation of optical properties, including reflection, refraction, polarization, phase, and frequency, has long been central to advancing photonic and optoelectronic technologies. However, existing electro-optical approaches rely on volatile mechanisms that require continuous power consumption. Here, we demonstrate strong, nonvolatile modulation of optical dispersion in monolayer tungsten disulfide (ML WS2) using patterned ferroelectric domains in aluminum scandium nitride (AlScN). By locally poling ferroelectric domains into opposite states, we achieve substantial manipulation of the complex refractive index (Delta n > 0.7, Delta k > 0.4) and excitonic energy shifts (~50 meV) in ML WS2, comparable to previous gate-tuning approaches while eliminating continuous power consumption. We introduce an asymmetric screening model that reveals how ferroelectric polarization induces carrier-density-dependent Coulomb screening, leading to distinct excitonic behaviors between electron- and hole-doped regions. Furthermore, we demonstrate a gate-free lateral p-n homojunction with a rectification ratio of 6e10^3, formed through spatial carrier redistribution. These findings establish ferroelectric/2D heterostructures as a powerful platform for nonvolatile optical dispersion engineering, enabling energy-efficient, reconfigurable photonic and optoelectronic devices.

physics.optics↗

arXiv2025

TAG: Tangential Amplifying Guidance for Hallucination-Resistant Sampling

Diffusion models achieve state-of-the-art image generation but often produce semantic inconsistencies, or hallucinations. Existing inference-time guidance methods rely on external signals or architectural modifications, adding computational overhead. We propose $\mathbf{T}$angential $\mathbf{A}$mplifying $\mathbf{G}$uidance $\mathbf{(TAG)}$, a training-free, architecture-agnostic, plug-and-play guidance method that operates purely on trajectory signals. TAG uses an intermediate sample as a projection basis and amplifies the tangential components of the estimated score to correct the sampling trajectory. A first-order Taylor analysis shows that this steers the state toward higher-probability regions of the data manifold, reducing inconsistencies and improving fidelity while adding negligible overhead to existing samplers. Code is available at our Project Page (https://hyeon-cho.github.io/TAG/).

cs.CV↗

arXiv2025

Kilobyte-Scale, Selector-Free, Temperature-Hard AlScN Ferroelectric Diode Crossbar Arrays

We report the fabrication and characterization of kilobyte-scale, selector-free, ferroelectric (FE) diode crossbar memory arrays based on aluminum scandium nitride (AlScN). Utilizing a fully CMOS back-end-of-line (BEOL) compatible process, we fabricated 2-kilobyte (128 $\times$ 128) arrays with device diameters down to 5 $\mu$m, achieving memory densities up to 2500 bits/mm$^2$. Large-scale electrical characterization across 1000 randomly selected devices reveals a yield rate of 95.2%, a tight switching voltage distribution with a coefficient of variation (CV) of 0.003, and consistent on/off ratios of around 10 with a CV of 0.27. We demonstrate selector-free read and program operations of the array, enabled by the high nonlinearity, rectification, and uniform switching behavior of the FE diodes. Furthermore, we verified consistent ferroelectric switching during array operation at temperatures up to 600 $^\circ$C. Our results highlight the potential of AlScN FE diode arrays for energy-efficient, high-density memory applications and lay the groundwork for future integration in compute-near-memory, high-temperature memory, and analog compute-in-memory systems.

cond-mat.mes-hall↗

arXiv2025

Demonstration of highly scaled AlScN ferroelectric diode memory with storage density > 100 Mbit/mm$^2$

Wurtzite nitride ferroelectric materials have emerged as promising candidates for next-generation memory applications due to their exceptional polarization properties and compatibility with conventional semiconductor processing techniques. Here, we demonstrate the first successful areal scaling of Aluminum Scandium Nitride (AlScN) ferroelectric diode (FeDiode) memory down to 40 nm device diameters while maintaining ON/OFF > 60. Using a 20 nm thick Al0.64Sc0.36N ferroelectric layer, we evaluate both metal-insulator-ferroelectric-metal (MIFM) and metal-ferroelectric-metal (MFM) architectures for scaled resistive memory devices. Our scaled devices exhibit an enhanced breakdown-to-coercive field ratio exceeding 2.6 due to increased breakdown field. The MIFM devices demonstrate stable 3-bit non-volatile multistate behavior with clearly distinguishable resistance states and retention exceeding 4*10^4 seconds at 85 C. By achieving more than a million-fold areal scaling with enhanced performance metrics, this work establishes AlScN-based FeDiode memory as a highly promising platform for non-volatile storage with potential for direct integration into CMOS technology.

cond-mat.mes-hall↗

arXiv2025

Write Cycling Endurance Exceeding 1010 in Sub-50 nm Ferroelectric AlScN

Wurtzite ferroelectrics, particularly aluminum scandium nitride (AlScN), have emerged as a promising materials platform for nonvolatile memories, offering high polarization values exceeding 100 uC/cm2. However, their high coercive fields (>3 MV/cm) have limited cycling endurance to ~107 cycles in previous reports. Here, we demonstrate unprecedented control of polarization switching in AlScN, achieving write cycling endurance exceeding 1010 cycles a thousand fold improvement over previous wurtzite ferroelectric benchmarks. Through precise voltage modulation in 45 nm thick Al0.64Sc0.36N capacitors, we show that while complete polarization reversal (2Pr ~ 200 uC/cm2) sustains ~108 cycles, partial switching extends endurance beyond 1010 cycles while maintaining a substantial polarization (>30 uC/cm2 for 2Pr). This exceptional endurance, combined with breakdown fields approaching 10 MV/cm in optimized 10 um diameter devices, represents the highest reported values for any wurtzite ferroelectric. Our findings establish a new paradigm for reliability in nitride ferroelectrics, demonstrating that controlled partial polarization and size scaling enables both high endurance and energy efficient operation.

cond-mat.mtrl-sci↗

arXiv2025

Low-voltage Ferroelectric Field-Effect Transistors with Ultrathin Aluminum Scandium Nitride and 2D channels

The continued evolution of CMOS technology demands materials and architectures that emphasize low power consumption, particularly for computations involving large scale data processing and multivariable optimization. Ferroelectric materials offer promising solutions through enabling dual-purpose memory units capable of performing both storage and logic operations. In this study, we demonstrate ferroelectric field effect transistors (FeFETs) with MoS2 monolayer channels fabricated on ultrathin 5 nm and 10 nm ferroelectric Aluminum Scandium Nitride (AlScN) films. By decreasing the thickness of the ferroelectric film, we achieve significantly reduced gate voltages (<3V) required to switch the conductance of the devices, enabling operation at low voltages compatible with advanced CMOS. We observe a characteristic crossover in hysteresis behavior that varies with film thickness, channel fabrication method, and environmental conditions. Through systematic investigation of multiple parameters including channel fabrication methods, dimensional scaling, and environmental effects, we provide pathways to improve device performance. While our devices demonstrate clear ferroelectric switching behavior, further optimization is required to enhance the ON/OFF ratio at zero gate voltage while continuing to reduce the coercive field of these ultrathin films.

physics.app-ph↗

arXiv2025

Towards Lossless Implicit Neural Representation via Bit Plane Decomposition

We quantify the upper bound on the size of the implicit neural representation (INR) model from a digital perspective. The upper bound of the model size increases exponentially as the required bit-precision increases. To this end, we present a bit-plane decomposition method that makes INR predict bit-planes, producing the same effect as reducing the upper bound of the model size. We validate our hypothesis that reducing the upper bound leads to faster convergence with constant model size. Our method achieves lossless representation in 2D image and audio fitting, even for high bit-depth signals, such as 16-bit, which was previously unachievable. We pioneered the presence of bit bias, which INR prioritizes as the most significant bit (MSB). We expand the application of the INR task to bit depth expansion, lossless image compression, and extreme network quantization. Our source code is available at https://github.com/WooKyoungHan/LosslessINR

cs.CV↗