arXiv subjects

Suhas Suresh Bharadwaj

Publications and source records attributed to Suhas Suresh Bharadwaj.

7 recordsLinked to original sources

arXiv2026

Adjoint-State Identifiability of Piezo-Tunable Valley Splitting in 2D Magnetic Heterostructures

Controlling valley degrees of freedom with mechanical strain is a promising approach for solid-state information processing. Current theoretical literature routinely predicts strain-tuned valley splitting at the microscopic level but rarely evaluates whether these quantum predictions remain statistically recoverable in realistic macroscopic devices. This manuscript establishes a fully classical, partial differential equation-constrained multiscale inverse framework for quantifying the device-level identifiability of predicted strain-tunable valley effects in two-dimensional magnetic heterostructures, demonstrated here for a molybdenum disulfide and chromium tribromide heterostructure. First-principles structural relaxations confirm a chiral $C_3$ point-group symmetry which mathematically reduces the relevant exchange-strain coupling tensor to a single scalar. A partial differential equation-constrained adjoint-state architecture successfully bridges continuum elastodynamics to valley-resolved anomalous Hall transport. Density functional theory yields a coupling estimate of $η\approx -0.07$ meV whose 95% confidence interval is consistent with zero. Evaluating this specific coupling magnitude against established thermal noise and velocity saturation limits defines a safe operating window bounded between 262.0 and 22,337.6 V/cm. Rather than asserting a confirmed nonzero material property this bounded operational window functions as a precise diagnostic threshold. Deploying this rigorous statistical identifiability framework provides a necessary mathematical filter to determine the true experimental viability of theoretically predicted two-dimensional materials before complex physical fabrication.

cond-mat.mes-hall↗

arXiv2026

Contact-Governed Macroscopic Signatures of Strain-Induced Valley Sorting in MoS$_2$ Field-Effect Transistors

Strain engineering is a leading route to control valley-selective transport in monolayer transition metal dichalcogenides. Whether this quantum effect survives inside a fully contacted device remains an open question. Quantum transport models capture the underlying valley deflection but are limited to isolated nanoscale fragments, leaving it unclear if a full device registers strain-induced valley sorting in the bulk, at the contacts, or at all. Here, the quantum valley-deflection tensor of strained monolayer MoS$_2$ is embedded directly into a macroscopic Poisson-drift-diffusion model, extending this physics to a complete, contact-inclusive device. Applying this framework to a 300~nm monolayer MoS$_2$ transistor with a 3~nm HfO$_2$ gate dielectric at 77~K, under a localized 6.14~T pseudo-magnetic strain field, shows the resulting 63.6\% suppression of the ON-state current is not a bulk property. It is set almost entirely by the source and drain metal-semiconductor interfaces. The full suppression is reproduced when only the 15~nm contact regions are strained and nearly vanishes when the channel interior alone is strained, identifying the metal contact as the dominant site of strain-induced valley physics. Observing it requires tunneling-transparent, degenerately-doped contacts, since classical Ohmic barriers overwhelm the topological signal. A transmission-line analysis reduces this behavior to a single valley-topological contact resistance ($R_{c,V}$) and a predictive inverse-length scaling law. This framework applies to any strain-engineered 2D-material heterostructure and can be tested with standard multi-length contact structures. Ultimately, these results identify the metal-semiconductor interface as the decisive location for engineering and detecting valley-selective transport in real 2D-material devices.

cond-mat.mes-hall↗

arXiv2026

The Post-Silicon Semiconductor Era: A Review of Physics, Synthesis, and Architectural Integration of Carbon Nanotube Field-Effect Transistors

Silicon CMOS scaling is approaching a set of hard physical limits. Direct source-to-drain quantum tunneling, subthreshold swing degradation, and the Dark Silicon thermal ceiling motivate the search for a new channel material. This review builds the case for single-walled carbon nanotubes (SWCNTs) as that material. We follow a continuous narrative from electronic structure theory through synthesis to integration. The SWCNT bandgap and near-ballistic transport limits are derived from the graphene zone-folding framework and Landauer-Büttiker formalism. We benchmark these theoretical limits against ideal coaxial electrostatic bounds to evaluate how the geometry suppresses short-channel effects before tunneling dominates. Comparing this framework against 5 nm experimental data illustrates the aggressive subthreshold degradation driven by source-to-drain tunneling. Furthermore, we derive the exact areal-density equivalence between 1D and 2D quantum capacitance. This demonstrates that close-packed arrays cannot close the dimensional gap to 2D materials, underscoring why superior carrier velocity and electrostatics must carry the CNTFET advantage. Next, we examine CoMoCAT growth and aqueous two-phase extraction against logic fabrication purity demands alongside contact engineering and doping. A closing techno-economic analysis weighs this picture against IEEE IRDS projections. Ultimately, materials purification, contact reliability, and bias temperature instability remain the practical barriers to commercial adoption.

cond-mat.mes-hall↗

arXiv2026

A Unified Physics-Informed Neural Network for Modeling Coupled Electro- and Elastodynamic Wave Propagation Using Three-Stage Loss Optimization

Physics-Informed Neural Networks present a novel approach in SciML that integrates physical laws in the form of partial differential equations directly into the NN through soft constraints in the loss function. This work studies the application of PINNs to solve a one dimensional coupled electro-elastodynamic system modeling linear piezoelectricity in stress-charge form, governed by elastodynamic and electrodynamic equations. Our simulation employs a feedforward architecture, mapping space-time coordinates to mechanical displacement and electric potential. Our PINN model achieved global relative L2 errors of 2.34 and 4.87 percent for displacement and electric potential respectively. The results validate PINNs as effective mesh free solvers for coupled time-dependent PDE systems, though challenges remain regarding error accumulation and stiffness in coupled eigenvalue systems.

cs.NE↗

arXiv2026

Chiplet-Based RISC-V SoC with Modular AI Acceleration

Achieving high performance, energy efficiency, and cost-effectiveness while maintaining architectural flexibility is a critical challenge in the development and deployment of edge AI devices. Monolithic SoC designs struggle with this complex balance mainly due to low manufacturing yields (below 16%) at advanced 360 mm^2 process nodes. This paper presents a novel chiplet-based RISC-V SoC architecture that addresses these limitations through modular AI acceleration and intelligent system level optimization. Our proposed design integrates 4 different key innovations in a 30mm x 30mm silicon interposer: adaptive cross-chiplet Dynamic Voltage and Frequency Scaling (DVFS); AI-aware Universal Chiplet Interconnect Express (UCIe) protocol extensions featuring streaming flow control units and compression-aware transfers; distributed cryptographic security across heterogeneous chiplets; and intelligent sensor-driven load migration. The proposed architecture integrates a 7nm RISC-V CPU chiplet with dual 5nm AI accelerators (15 TOPS INT8 each), 16GB HBM3 memory stacks, and dedicated power management controllers. Experimental results across industry standard benchmarks like MobileNetV2, ResNet-50 and real-time video processing demonstrate significant performance improvements. The AI-optimized configuration achieves ~14.7% latency reduction, 17.3% throughput improvement, and 16.2% power reduction compared to previous basic chiplet implementations. These improvements collectively translate to a 40.1% efficiency gain corresponding to ~3.5 mJ per MobileNetV2 inference (860 mW/244 images/s), while maintaining sub-5ms real-time capability across all experimented workloads. These performance upgrades demonstrate that modular chiplet designs can achieve near-monolithic computational density while enabling cost efficiency, scalability and upgradeability, crucial for next-generation edge AI device applications.

cs.AR↗

arXiv2025

Simultaneous photonic and phononic bandgaps in a hexagonal lattice geometry with gradually transforming circular-to-triangular air gap holes

The integration of photonic and phononic bandgaps within a single scalable architecture promises transformative advances in optomechanical and acousto-optic devices. Here, we design and simulate a two-dimensional hexagonal lattice in silicon with air-gap holes that transition smoothly from circular to triangular via tuneable geometrical parameters including air-gap hole radius (R) and tether length (l). By independently varying these two parameters, we systematically explore diverse honeycomb lattice geometries and their bandgap properties. This transformation from circular to triangular air-gap holes enables suppression of both electromagnetic and elastic wave modes through Bragg scattering and symmetry modulation. We demonstrate that systematic variation of R and l allows tuning of photonic and phononic bandgaps upto 49.7% and 24.8% respectively. This possibility of geometrically tuning bandgaps provide a strong foundation for applications in Bragg filters, sensors etc. without the need for complex defects or exotic materials.

physics.optics↗

arXiv2025

On the Generation of Phononic Frequency Combs Using Defect Modes of Phononic Crystals

This paper proposes a method for generating phononic frequency combs (PFCs) using defect-localized modes in a two-dimensional hexagonal phononic crystal. Localized vibration modes from a singular point defect produce evenly spaced spectral lines corresponding to PFCs. Numerical modelling reveals robust energy transfer under a single-tone drive, generating spectral sidebands. These results demonstrate defect engineering in phononic crystals as a tunable platform for PFC generation with significant applications in high-resolution sensing, timing, and quantum-acoustic technologies.

physics.app-ph↗