arXiv subjects

Nahid Hosseini

Publications and source records attributed to Nahid Hosseini.

3 recordsLinked to original sources

arXiv2026

Agentic AI for operating scientific instruments for nanoscale characterization

Operating a scientific instrument such as an atomic force microscope (AFM) requires continuous expert decision-making. A trained user defines the experimental intent, translates it into instrument commands, assesses incoming data, adjusts imaging parameters, and post-processes the final image. Existing automation usually addresses only parts of this workflow through hard-coded routines, task-specific controllers, or trained machine-learning models. Here we present an agentic-AI framework that operates the executable part of the AFM workflow using a general-purpose, tool-augmented large language model connected to instrument functions through the Model Context Protocol (MCP). The framework consists of 3 MCP-based agents: AFM Messenger converts natural-language instructions into checked instrument commands; AFM Pilot assesses image quality through a large language model (LLM) and, if necessary, adapts imaging parameters; and AFM Doctor diagnoses image artifacts and applies transparent post-processing from a pre-approved tool set. Because the language model performs image assessment rather than a fixed scalar objective or external optimizer, the same strategy can be applied across sample types and imaging modes without specific retraining. Safe hardware operation is enforced through an ambiguity check layer before execution. Benchmarking against fine-tuned and off-the-shelf tool-using models shows that this guarded execution layer, rather than model capability alone, reduces wrong-command execution to zero. In live experiments on different samples, AFM Pilot matched expert operators in image quality, iteration count, and tuning time, with no significant difference. These results demonstrate a safe route to agentic operation of scientific instruments, where experimental intent remains human-defined while command execution, image-based tuning, and post-processing are delegated to AI agents.

cs.AI↗

arXiv2026

In-situ correlative SEM/KPFM for semiconductor devices and 2D heterostructures

Correlative nanoscale surface characterization benefits from simultaneously measuring electronic and structural properties in the same environment, a capability that is essential for modern-day materials science and semiconductor failure analysis. In-situ AFM-SEM measurements facilitated by self-sensing cantilevers offer great potential here; however, they are limited due to their inherent capacitive crosstalk. Here, we demonstrate for the first time the in-situ implementation of single-pass heterodyne Kelvin probe force microscopy inside a scanning electron microscope, using piezo-resistive cantilevers. We overcome the capacitive crosstalk prevalent in piezo-resistive cantilevers by demodulating excitation and detection to simultaneously map surface topography and contact potential difference for correlation with compositional analysis. We systematically compare different operational modes of this heterodyne technique, elucidating their spatial resolution, signal sensitivity, and signal-to-noise ratio. The integrated approach yields exceptional signal quality and reveals how electron beam scan parameters can directly influence surface potential contrast. We demonstrate this correlative analysis workflow on two-dimensional heterostructures and semiconductor circuits. This work establishes a robust and versatile correlative imaging mode for in-situ Kelvin force and topography imaging inside a scanning electron microscope for next-generation semiconductor device analysis and materials science.

cond-mat.mes-hall↗

arXiv2023

A hybrid polymer/ceramic/semiconductor fabrication platform for high-sensitivity fluid-compatible MEMS devices with sealed integrated electronics

Active microelectromechanical systems can couple the nanomechanical domain with the electronic domain by integrating electronic sensing and actuation mechanisms into the micromechanical device. This enables very fast and sensitive measurements of force, acceleration, or the presence of biological analytes. In particular, strain sensors integrated onto MEMS cantilevers are widely used to transduce an applied force to an electrically measurable signal in applications like atomic force microscopy, mass sensing, or molecular detection. However, the high Young's moduli of traditional cantilever materials (silicon or silicon nitride) limit the thickness of the devices, and therefore the deflection sensitivity that can be obtained for a specific spring constant. Using softer materials such as polymers as the structural material of the MEMS device would overcome this problem. However, these materials are incompatible with high-temperature fabrication processes often required to fabricate high quality electronic strain sensors. We introduce a pioneering solution that seamlessly integrates the benefits of polymer MEMS technology with the remarkable sensitivity of strain sensors, even under high-temperature deposition conditions. Cantilevers made using this technology are inherently fluid compatible and have shown up to 6 times lower force noise than their conventional counterparts. We demonstrate the benefits and versatility of this polymer/ceramic/semiconductor multi-layer fabrication approach with the examples of self-sensing AFM cantilevers, and membrane surface stress sensors for biomolecule detection.

physics.app-ph↗