arXiv subjects

Asif Khan

Publications and source records attributed to Asif Khan.

At least 19 recordsLinked to original sources

arXiv2026

Connectivity Augmentation of Plane Graphs

We study the problem of connectivity augmentation of a planar graph, while preserving planarity. This problem is motivated by many real-world settings such as road-networks, power-networks etc. In these settings, it is crucial to preserve the original planar embedding after augmentation. In 2009, Gutwenger and Mutzel gave a constructive algorithm showing that a connected planar graph with a fixed embedding (a plane graph) can be optimally augmented to a biconnected graph without crossings while preserving the embedding. We further this line of research, by giving an algorithm that computes a minimum set of edges that makes a connected plane graph 2-edge-connected in $O(|V|(1+\alpha(|V|)))$ time and linear space, where $\alpha$ is the inverse Ackermann function.

cs.DS↗

arXiv2026

Capacity and Redundancy Trade-offs in Multi-Task Learning

In multi-task learning (MTL) negative transfer is often considered as an optimization artifact, but it can also be viewed as a consequence of limited shared capacity and weak task redundancy. We investigate this effect through a Capacity--Redundancy (CR) identity that decomposes the sum of per-task predictive informations into joint predictive information that includes label redundancy defined via total correlation (TC), and a residual coupling term that quantifies interference left unresolved by the shared representation. Additionally, we show two key results: (i) a clustering-gap decomposition that gives a necessary and sufficient condition for clustered sharing to outperform global sharing, and (ii) a gradient--TC bridge in a Gaussian multi-task model that formally justifies gradient cosine similarity as a proxy for redundancy ordering. Empirically, we estimate the residual coupling $\Delta$ from validation residual correlations, showing that clustered LoRA substantially reduces $\widehat{\Delta}$, outperforms size-matched random partitions, and results in statistically significant gains with multi-seed confidence intervals.

cs.LG↗

arXiv2026

Thermal Processing Limits in Oxide-Channel Ferroelectric Field Effect Transistors

In this work, we report a systematic study of the impact of high-temperature post-capping thermal annealing on the memory characteristics of Oxide-semiconductor channel ferroelectric field-effect transistors (OS-FeFETs). Using an identical engineered ferroelectric gate stack 8nm Hf0.5Zr0.5O2 (HZO) / 3 nm Al2O3 / 8 nm HZO (8/3/8) and a hybrid capping layer (3 nm HfO2 + 3 nm Al2O3), 10 percent Ga doped InO (IGO) channel and 4 percent W doped InO (IWO) channel FeFETs remain functional after annealing at temperatures up to 650 C for durations of up to 30 min and 10 min, respectively; further annealing results in irreversible loss of conduction and device failure. Detailed electrical analysis reveals that the MW enhancement originates from a preferential positive shift in the erased-state threshold voltage, while the programmed-state threshold voltage remains comparatively stable. Grazing-incidence X-ray diffraction measurements further indicate structural evolution in the IWO and IGO oxide channels with increasing annealing temperature, supporting the observed electrical trends.

cond-mat.mtrl-sci↗

arXiv2026

A Promising Ohmic Contacts Approach for High-Al AlxGa1-xN (x>0.6) Channel HEMTs with AlN/GaN Digital Alloy Channel

In this paper we report a novel ohmic contact formation scheme for Extreme Bandgap (EBG) AlxGa1-xN (x>0.6) channel HEMTs with undoped barrier layers. Our approach consists of using a new low temperature (LT) pulsed metal-organic chemical vapor deposition (PMOCVD) doping scheme for the n++-GaN regrown contacts and an AlxGa1-xN digital alloy (DA) channel layer comprising short period super lattices (SPSL) of AlN and GaN. Pulsed growth and doping yield a sheet resistivity which is a factor of 3-5 lower than that of conventional doped n++-GaN layers grown under identical conditions. Moreover, the regrown n++-GaN layer has no hetero-barrier with the GaN layers of the AlxGa1-xN DA channel. These innovations led to MOCVD regrown linear ohmic contacts and a record-low contact resistance Rc ~6.5 {\Omega}-mm to the Al0.62Ga0.38N DA channel layer of a HEMT with AlN barrier layer.

physics.app-ph↗

arXiv2026

Dynamic Planar Graph Isomorphism is in DynFO

Consider two planar graphs which are subject to edge insertions and deletions. We show that whether the two graphs are isomorphic can be maintained with first-order logic formulas and auxiliary data of polynomial size. This places the dynamic planar graph isomorphism problem into the dynamic descriptive complexity class DynFO. As a consequence, there is a dynamic constant-time parallel algorithm with polynomial-size auxiliary data which maintains whether two dynamic planar graphs are isomorphic.

cs.LO↗

arXiv2026

Engineering Wake-Up-Free Ferroelectric Capacitors with Enhanced High-Temperature Reliability

We systematically explore the design space of ferroelectric hafnium-zirconium oxide (H0.5Z0.5O or HZO) heterostructures for reliable high temperature operation. HZO films are deposited using thermal and plasma-enhanced atomic layer deposition (Th-ALD and PE-ALD) on tungsten (W) and titanium nitride (TiN) bottom electrodes (BE), while maintaining identical top electrodes. We demonstrate that PE-ALD HZO capacitors integrated with W BE exhibit wake-up-free switching up to 125C, along with significantly improved endurance compared to Th-ALD HZO/W devices across a wide temperature range (85-125C). By decoupling the contributions of the plasma-deposited HZO film and the oxidized bottom interface inherently formed during PE-ALD, we identify the oxidized W interfacial layer (WOx) as the primary factor governing endurance enhancement and wake-up suppression at elevated temperatures, while the PE-ALD HZO film provides secondary benefits in reducing wake-up. In contrast, PE-ALD HZO capacitors fabricated on TiN BE show no substantial improvement in wake-up behavior or endurance relative to Th-ALD HZO/TiN devices, despite the formation of an unintentional TiOxNy interfacial layer, and instead exhibit degraded polarization. This difference arises from the significantly weaker endurance enhancement and no wake-up suppression provided by oxidized TiN compared to oxidized W under comparable oxidation conditions. Overall, PE-ALD HZO films enable superior ferroelectric performance at elevated temperatures only when deposited on W BE, while Th-ALD HZO films remain a viable option for high temperature operation on TiN BE. These findings clarify the interplay between deposition technique, electrode chemistry, and interfacial oxidation, and provide design guidelines for integrating ferroelectric memories into monolithic 3D systems under stringent thermal constraints.

cond-mat.mtrl-sci↗

arXiv2026

ALD Oxidant as A Tuning Knob for Memory Window Expansion in Ferroelectric FETs for Vertical NAND Applications

Dielectric inserts are widely used to expand the memory window (MW) in ferroelectric FETs (FeFETs) for vertical NAND applications, with prior efforts focused primarily on material selection and stack positioning. Here, we demonstrate that the ALD oxidant used for the Al2O3 interlayer serves as a process-level tuning knob for MW engineering. H2O-grown Al2O3 yields a significantly larger MW (7-8 V) compared to O3 (4 V) for both gate-injection (12/3) and tunnel dielectric (8/3/8) configurations. While the tunnel dielectric (8/3/8) stack maintains robust retention up to 1e4s at 125C despite the larger MW, the gate-injection (12/3) configuration exhibits pronounced retention degradation for the H2O case. The enhanced MW is attributed to higher interlayer leakage associated with H2O-based ALD. These results establish oxidant choice as a key process parameter for co-optimizing MW and retention in ferroelectric NAND technologies.

cond-mat.mtrl-sci↗

arXiv2026

Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model

Ferroelectric field-effect transistors (FeFET)-based vertical NAND (Fe-VNAND) has emerged as a promising candidate to overcome z-scaling limitations with lower programming voltages. However, the data retention of 3D Fe-VNAND is hindered by the complex interaction between charge detrapping and ferroelectric depolarization. Developing optimized device designs requires exploring an extensive parameter space, but the high computational cost of conventional Technology Computer-Aided Design (TCAD) tools makes such wide-scale optimization impractical. To overcome these simulation barriers, we present a Physics-Informed Neural Operator (PINO)-based AI surrogate model designed for high-efficiency prediction of threshold voltage (Vth) shifts and retention behavior. By embedding fundamental physical principles into the learning architecture, our PINO framework achieves a speedup exceeding 10000x compared to TCAD while maintaining physical accuracy. The resulting surrogate provides a physics-consistent data engine for compact model parameter extraction and look-up-table (LUT) generation, directly supporting reliability-aware SPICE simulation of Fe-VNAND. This study demonstrates the model's effectiveness on a single FeFET configuration, serving as a pathway toward modeling the retention loss mechanisms.

cs.LG↗

arXiv2026

ALD-Derived WO3-x Leads to Nearly Wake-Up-Free Ferroelectric Hf0.5Zr0.5O2 at Elevated Temperatures

Breaking the memory wall in advanced computing architectures will require complex 3D integration of emerging memory materials such as ferroelectrics-either within the back-end-of-line (BEOL) of CMOS front-end processes or through advanced 3D packaging technologies. Achieving this integration demands that memory materials exhibit high thermal resilience, with the capability to operate reliably at elevated temperatures such as 125C, due to the substantial heat generated by front-end transistors. However, silicon-compatible HfO2-based ferroelectrics tend to exhibit antiferroelectric-like behavior in this temperature range, accompanied by a more pronounced wake-up effect, posing significant challenges to their thermal reliability. Here, we report that by introducing a thin tungsten oxide (WO3-x) layer-known as an oxygen reservoir-and carefully tuning its oxygen content, ultra-thin Hf0.5Zr0.5O2 (5 nm) films can be made robust against the ferroelectric-to-antiferroelectric transition at elevated temperatures. This approach not only minimizes polarization loss in the pristine state but also effectively suppresses the wake-up effect, reducing the required wake-up cycles from 105 to only 10 at 125C- a qualifying temperature for back-end memory integrated with front-end logic, as defined by the JEDEC standard. First-principles density functional theory calculations reveal that WO3 enhances the stability of the ferroelectric orthorhombic phase at elevated temperatures by increasing the tetragonal-to-orthorhombic phase energy gap, and promoting favorable phonon mode evolution, thereby supporting o-phase formation under both thermodynamic and kinetic constraints.

cond-mat.mtrl-sci↗

arXiv2026

The First Switch Effect in Ferroelectric Field-Effect Transistors

In this work, a ferroelectric field-effect transistor (FEFET) is systematically characterized and compared with an equivalent standard MOSFET with an equivalent oxide thickness. We show that these two devices, with a silicon channel, exhibit similar pristine state transfer characteristics but starkly different endurance characteristics. In contrast to the MOSFET, the FEFET shows a significant increase in sub-threshold swing in the first write pulse. Based on this, we reveal that this first write pulse (cycle 1) generates more than half of the total traps generated during the fatigue cycling in FEFETs. We call this the 'First Switch Effect'. Further, by polarizing a pristine FEFET step by step, we demonstrate a direct correlation between the switched polarization and interface trap density during the first switch. Through charge pumping measurements, we also observe that continued cycling generates traps more towards the bulk of the stack, away from the Si/SiO2 interface in FEFETs. We establish that: (1) the first switch effect leads to approximately 50% of the total trap density (Nit) near the Si/SiO2 interface until memory window closure; and (2) further bipolar cycling leads to trap generation both at and away from Si/SiO2 interface in FEFETs.

cond-mat.mtrl-sci↗

arXiv2025

Vertical NAND in a Ferroelectric-driven Paradigm Shift

Over the past decades, the relentless scaling and mass production of flash memory have underpinned the data-centric era. Yet charge-trap-based 3D NAND flash is now constrained by intrinsic physical and architectural limits, including reliability degradation at the device level, high operating power at the array level, and vertical scaling saturation at the system level. These bottlenecks hinder further advances in storage density and energy efficiency required by memory-centric computing. This Perspective outlines how coupling ferroelectric polarization with charge trapping can reconfigure the foundations of flash memory. In these hybrid architectures, polarization offers an energy-efficient pathway for charge modulation through enhanced Fowler-Nordheim tunneling, while trapped charges reinforce polarization-driven states to ensure stability. Such synergistic dynamics enable low-voltage operation and integration beyond one thousand layers without compromising process compatibility. We discuss the material, device, and architectural transitions required to realize this hybrid technology and chart future research directions to overcome the remaining scaling bottlenecks. Hybrid ferroelectric NAND extends conventional flash toward a scalable and energy-efficient platform, marking a paradigm shift for next-generation non-volatile memory.

physics.app-ph↗

arXiv2025

Fast exact algorithms via the Matrix Tree Theorem

Fast exact algorithms are known for Hamiltonian paths in undirected and directed bipartite graphs through elegant though involved algorithms that are quite different from each other. We devise algorithms that are simple and similar to each other while having the same upper bounds. The common features of these algorithms is the use of the Matrix-Tree theorem and sieving using roots of unity. Next, we use the framework to provide alternative algorithms to count perfect matchings in bipartite graphs on $n$ vertices, i.e., computing the $\{0,1\}$-permanent of a square $n/2 \times n/2$ matrix which runs in a time similar to Ryser. We demonstrate the flexibility of our method by counting the number of ways to vertex partition the graph into $k$-stars (a $k$-star consist of a tree with a root having $k-1$ children that are all leaves). Interestingly, our running time improves to $O^*((1+\epsilon_k)^n)$ with $\epsilon_k \rightarrow 0$ as $k \rightarrow \infty$. As an aside, making use of Bj\"orklund's algorithm for exact counting perfect matchings in general graphs, we show that the count of maximum matchings can be computed in time $O^*(2^\nu)$ where $\nu$ is the size of a maximum matching. The crucial ingredient here is the famous Gallai-Edmonds decomposition theorem. All our algorithms run in polynomial space.

cs.DS↗

arXiv2025

FeNOMS: Enhancing Open Modification Spectral Library Search with In-Storage Processing on Ferroelectric NAND (FeNAND) Flash

The rapid expansion of mass spectrometry (MS) data, now exceeding hundreds of terabytes, poses significant challenges for efficient, large-scale library search - a critical component for drug discovery. Traditional processors struggle to handle this data volume efficiently, making in-storage computing (ISP) a promising alternative. This work introduces an ISP architecture leveraging a 3D Ferroelectric NAND (FeNAND) structure, providing significantly higher density, faster speeds, and lower voltage requirements compared to traditional NAND flash. Despite its superior density, the NAND structure has not been widely utilized in ISP applications due to limited throughput associated with row-by-row reads from serially connected cells. To overcome these limitations, we integrate hyperdimensional computing (HDC), a brain-inspired paradigm that enables highly parallel processing with simple operations and strong error tolerance. By combining HDC with the proposed dual-bound approximate matching (D-BAM) distance metric, tailored to the FeNAND structure, we parallelize vector computations to enable efficient MS spectral library search, achieving 43x speedup and 21x higher energy efficiency over state-of-the-art 3D NAND methods, while maintaining comparable accuracy.

cs.AR↗

arXiv2025

Sub-nm2 ferroelectric domains via charged 180 degree walls in ZrO2

Flat phonon bands in fluorite ferroelectrics (HfO2 or ZrO2) shrink polar domains laterally to an irreducible half-unit-cell width (0.27 nm) within which the vertical arrangement of dipoles is expected to remain uniform. We report on the direct observation of nonuniform and nearly discrete vertical arrangements of dipoles in ZrO2 thin films consisting of closely spaced head-to-head (HH) and tail-to-tail (TT) charged 180 degree walls, each exhibiting a distinct bulk-like structure. These charged domain walls (CDWs) further compress the irreducibly narrow, laterally stacked domains vertically to a thickness of 1-2.75 nm, yielding in-plane domains with sub-nm2 footprints-among the smallest ever reported for any ferroelectric material. The HH and TT walls form due to their flat longitudinal optical (LO) polar bands and are electrostatically stabilized by bound-charge compensation via interstitial oxygen atoms, which act as natural structural defects at the HH walls. Moreover, these walls are predicted to be conducting and to exhibit ultralow propagation barriers, with HH walls (1.6 meV) being far more mobile than TT walls (22.3 meV), indicating strong potential for low-voltage, domain-wall-based nanoelectronics.

cond-mat.mtrl-sci↗

arXiv2025

A Full Spectrum of 3D Ferroelectric Memory Architectures Shaped by Polarization Sensing

Ferroelectric memories have attracted significant interest due to their non-volatile storage, energy efficiency, and fast operation, making them prime candidates for future memory technologies. As commercial Dynamic Random Access Memory (DRAM) and NAND flash memory are transiting or have moved toward three-dimensional (3D) integration, 3D ferroelectric memory architectures are also emerging, provided they can achieve a competitive position within the modern memory hierarchy. Given the excellent scalability of ferroelectric HfO2, various dense 3D integrated ferroelectric memory architectures are feasible, each offering unique strengths and facing distinct challenges. In this work, we present a comprehensive classification of 3D ferroelectric memory architectures based on polarization sensing methods, highlighting their critical role in shaping memory cell design and operational efficiency. Through a systematic evaluation of these architectures, we develop a unified framework to assess their advantages and trade-offs. This classification not only enhances the understanding of current 3D ferroelectric memory technologies but also lays the foundation for designing next-generation architectures optimized for advanced computing and high-performance applications.

cs.ET↗

arXiv2025

Neural Network Operator-Based Fractal Approximation: Smoothness Preservation and Convergence Analysis

This paper presents a new approach of constructing $\alpha$-fractal interpolation functions (FIFs) using neural network operators, integrating concepts from approximation theory. Initially, we construct $\alpha$-fractals utilizing neural network-based operators, providing an approach to generating fractal functions with interpolation properties. Based on the same foundation, we have developed fractal interpolation functions that utilize only the values of the original function at the nodes or partition points, unlike traditional methods that rely on the entire original function. Further, we have constructed $\alpha$-fractals that preserve the smoothness of functions under certain constraints by employing a four-layered neural network operator, ensuring that if $f \in C^{r}[a,b]$, then the corresponding fractal $f^{\alpha} \in C^{r}[a,b]$. Furthermore, we analyze the convergence of these $\alpha$-fractals to the original function under suitable conditions. The work uses key approximation theory tools, such as the modulus of continuity and interpolation operators, to develop convergence results and uniform approximation error bounds.

cs.LG↗

arXiv2025

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis; improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and ultimately enabling AI-empowered virtual cells.

q-bio.OT↗

arXiv2024

Boundary Interpolation on Triangles via Neural Network Operators

The primary objective of this study is to develop novel interpolation operators that interpolate the boundary values of a function defined on a triangle. This is accomplished by constructing New Generalized Boolean sum neural network operator $\mathcal{B}_{n_1, n_2, \xi }$ using a class of activation functions. Its interpolation properties are established and the estimates for the error of approximation corresponding to operator $\mathcal{B}_{n_1, n_2, \xi }$ is computed in terms of mixed modulus of continuity. The advantage of our method is that it does not require training the network. Instead, the number of hidden neurons adjusts the weights and bias. Numerical examples are illustrated to show the efficacy of these newly constructed operators. Further, with the help of MATLAB, comparative and graphical analysis is given to show the validity and efficiency of the results obtained for these operators.

math.NA↗