Time-series image stacks drift, distort, or evolve in appearance during acquisition — particularly in fast-process studies, unstable acquisitions, and semiconductor SEM datasets. Classical rigid registration stops being enough. NeuralSoftX combines classical registration with ML-based correction to handle stacks where morphology itself is changing.
Typical data
Time-series SEM, STEM, or TEM images with rigid and non-rigid motion, scan artefacts, or evolving morphology.
Machinery
Classical registration plus custom ML models; simulation-backed evaluation where needed.
Tilt-series reconstructions degrade quickly when raw projections are noisy, misaligned, distorted, or affected by acquisition instabilities. NeuralSoftX builds pre-reconstruction pipelines that restore, align, and compensate tilt-series data before the volume is built — including support for low-dose and high-resolution acquisitions.
Typical data
STEM or TEM tomography projections, low-dose and high-resolution datasets, beam-sensitive specimens.
Machinery
Physics-informed per-projection restoration, custom alignment and compensation, and MULTEM for simulation-backed validation.
Single-shot images can be too noisy or too distorted for reliable interpretation, but longer acquisitions cost time, throughput, or specimen integrity. NeuralSoftX delivers restoration as lightweight ONNX-based inference tools that can run on CPU, NVIDIA GPU, or DirectML — adaptable to instrument-specific conditions. The underlying method is published in npj Computational Materials (2024).
Typical data
SEM, STEM, and TEM images acquired under low-dose, low-signal, or fast-scan conditions.
Machinery
Physics-informed single-shot restoration delivered as ONNX-packaged inference (CPU, NVIDIA GPU, or DirectML).
CBED and 4D-STEM pipelines become unstable when diffraction patterns are miscentred, noisy, or difficult to process with standard routines. NeuralSoftX provides preprocessing that centres, restores, and conditions diffraction data before it enters ptychography or quantitative diffraction analysis.
Typical data
CBED and 4D-STEM datasets for ptychography, diffraction analysis, and quantitative microscopy.
Machinery
Sub-pixel centring, restoration, and joint preprocessing recipes for difficult diffraction data.
NeuralSoftX builds segmentation workflows across the full EM stack, matching each category to the EM-literature vocabulary fab and R&D teams already use. The list below is organised by what the network is actually locating — atoms, phases, particles, fibres, cracks, contaminants, device features — not by generic computer-vision jargon.
- Atom-column detection and localisation — HAADF-STEM, HRSTEM, HRTEM — for catalyst nanoparticles, high-entropy alloys, ferroelectric thin films, and interfacial reconstructions in semiconductor and battery R&D.
- Phase, grain, and domain segmentation — HRTEM / HRSTEM at atomic resolution for crystallographic phases and polymorphs; SEM / BSE-SEM at mesoscale for microstructural constituents (ferrite / pearlite / martensite, alloy phases, additive-manufacturing microstructures).
- Nanoparticle / particle segmentation — SEM, STEM, TEM — instance-level segmentation for catalyst sizing, pharmaceutical nanoparticle QC, powder-metallurgy feedstock, pigment analysis, and battery-electrode particle statistics.
- Fibre and filament segmentation — SEM predominantly — diameter, orientation, and network statistics for electrospun mats, fibre-reinforced composites, filter media, and nanocellulose products.
- Crack, void, and fractography segmentation — SEM — porosity and crack QA for L-PBF / DED additive manufacturing, fatigue-life failure analysis, weld-root inspection, and post-mortem fractography.
- Technical-cleanliness particle analysis — SEM-EDS under ISO 16232 / VDA 19 workflows — automotive fluid-circuit, brake, and transmission release testing, aerospace cleanliness, precision hydraulics.
- Defect and damage segmentation in 2D materials — HAADF-STEM, ABF-STEM, aberration-corrected HRTEM — point and line defects in MoS2, WSe2, graphene, and h-BN for monolayer-semiconductor device physics and quantum-emitter engineering.
- Semiconductor defect inspection and metrology — CD-SEM contour / LER-LWR extraction, e-beam inspection (EBI) nuisance and killer-defect triage, EUV stochastic-defect detection, and cross-sectional TEM / STEM FinFET / GAA interface delineation.
- Biological and volume-EM segmentation — SBF-SEM, FIB-SEM, serial-section TEM — organelle and membrane segmentation for phenotyping and connectomics-adjacent workflows (listed as capability; cryo-EM single-particle picking flagged as adjacent).
Typical data
SEM, BSE-SEM, HRSTEM, HAADF-STEM, HRTEM, 4D-STEM, SBF-SEM, FIB-SEM — across atomic, mesoscale, and volumetric length scales.
Delivery
Workflow per task category: dense semantic segmentation, instance segmentation (Mask R-CNN class), keypoint heatmap regression (atom-column detection), or flood-filling segmentation (volume EM). Browser-based review interfaces integrated where operator-in-the-loop review matters.
Industrial electron-microscopy datasets rarely carry enough labelled examples to train robust models directly. NeuralSoftX closes that gap by simulating training data at full physical fidelity — and this works because both the electron–specimen interaction and the detector recording process are well-understood physical processes. The generated data is not a textbook approximation; it matches a specific instrument, detector, and acquisition regime.
Every simulated image is the output of a full forward model: multislice electron–specimen scattering (elastic and inelastic, frozen-lattice thermal diffuse scattering for HAADF) via MULTEM; aberration-function modelling and probe simulation for STEM; detector physics covering point-spread function (PSF), modulation transfer function (MTF), detective quantum efficiency (DQE), Poisson electron shot noise, Gaussian readout noise, gain and dark reference; and scan-distortion modelling for fast-scan / slow-scan STEM acquisitions. Scattering potentials use the Lobato–Van Dyck 2014 parameterisation (Acta Crystallographica A), roughly an order of magnitude more accurate across the periodic table than Doyle–Turner or Kirkland.
Typical data
TEM, STEM, HAADF-STEM, CBED, 4D-STEM, EFTEM, and EELS acquisition regimes where simulation can approximate the relevant scattering and detection physics.
Delivery
Synthetic training corpora, pretrained models, sim-only or sim-pretrain-then-real-finetune pipelines, or full simulation-driven method development. Engine: MULTEM (C++/CUDA multislice, GPL-3.0, actively maintained since 2014, published in Ultramicroscopy 2015 and 2016).