scope 1 emissions, environmental & sustainability
Scope 1 emissions are direct greenhouse gas emissions from fab operations including process gases combustion and fugitive emissions.
3,145 technical terms and definitions
Scope 1 emissions are direct greenhouse gas emissions from fab operations including process gases combustion and fugitive emissions.
Scope 2 emissions are indirect emissions from purchased electricity steam heating and cooling consumed by fab operations.
Scope 3 emissions include indirect supply chain emissions from materials transportation business travel and product use.
Score distillation sampling uses diffusion model gradients to optimize 3D representations.
Train by matching score functions.
Train by matching data scores.
Unified framework connecting scores and diffusion.
Generate by learning score function.
Generate CAMs without gradients.
Evaluate quality of docking poses.
Scribble conditioning interprets rough sketches as spatial guidance for generation.
Condition on rough sketches.
Scrubber systems remove hazardous gases from process tool exhaust through chemical reaction or adsorption before atmospheric release.
Stable Diffusion upscaling.
Soft Defect Localization uses stimulation and imaging techniques to reveal intermittent or parametric failures.
SE(3) Transformer processes 3D point clouds and molecular structures with equivariance to rotation and translation through spherical harmonics.
Transformers with 3D symmetries.
SE(3)-equivariant GNNs process molecular geometries maintaining equivariance to rotations translations and reflections.
Generate seamlessly tileable images.
Search space design determines the set of possible architectures balancing expressiveness with search efficiency.
Seasonal state space models represent seasonality through trigonometric or dummy variable state equations.
Secure aggregation computes collective statistics without revealing individual contributions.
Secure multi-party computation allows joint computation without revealing private inputs.
Seebeck effect imaging detects voltage gradients from thermal variations revealing resistive defects and current paths.
Seeds yield model accounts for systematic and random defects with separate density and clustering parameters.
Process segments recurrently.
Condition on semantic maps.
Choose which knowledge to transfer.
Selective prediction abstains from answering when confidence is insufficient.
Choose when to abstain from prediction.
Selective SSMs dynamically adjust state transitions based on input content.
Self-alignment improves models using their own generated critiques and revisions.
Combine capsules with attention.
Self-attentive Hawkes processes combine transformer architectures with temporal point process likelihoods for scalable event sequence modeling.
Model critiques its own outputs.
Distill model into itself.
Self-distillation uses model's own predictions as soft targets improving generalization.
Ensemble predictions for consistency.
Architectures designed for interpretability.
Use gates controlled by input.
Model temperature rise in device.
Self-Instruct generates training data by prompting models to create instructions and responses.
Self-monitoring tracks progress evaluating whether actions advance toward goals.
Self-paced learning automatically selects training samples based on current model capability starting with easier examples.
Let model select training order.
Self-supervised graph neural networks learn representations through pretext tasks without labeled data.
DINO MAE BEiT methods.
Self-training is a semi-supervised method where models generate pseudo-labels for unlabeled data to augment training sets iteratively.
Use model predictions on unlabeled data as training labels.
Alternative molecular representation.