29,259 technical terms and definitions
excursion alarm threshold tuning prediction machine learning, excursion alarm, threshold tuning prediction, spc, excursion alarm threshold tuning prediction, alarm threshold
Excursion Alarm Threshold Tuning Prediction with Machine Learning Introduction & Motivation Excursion Alarm Threshold Tuning Prediction with Machine Learning addresses a central…
production
Excursion Detection is the automated, real-time identification that a semiconductor process has deviated beyond its qualified operating envelope — the triggering event that…
yield enhancement
Excursion Detection is identification of abnormal process or yield behavior that deviates from expected control limits - It provides early warning for events that can rapidly…
production
Excursion Management is the operational framework encompassing the detection, containment, root cause analysis, corrective action, and release protocols for process excursions…
production
Excursion Response (OCAP — Out of Control Action Plan) is the pre-documented, step-by-step response procedure that operators and engineers execute immediately upon receiving…
nlp
Executable semantic parsing is the NLP task of converting natural language utterances into executable formal representations — such as SQL queries, API calls, Python code…
code ai
Execution feedback is a code AI paradigm where generated code is actually executed, and any resulting errors, outputs, or test results are fed back to the model to iteratively…
ai agents
Execution Trace is a step-by-step causal record of how an agent progressed from initial state to final output - It is a core method in modern semiconductor AI-agent engineering…
biden, safety
The Biden Executive Order on AI (October 2023) is the first major binding U.S. federal directive on artificial intelligence safety, security, and trust — establishing reporting…
content creation
Executive summary generation is the use of AI to automatically create concise, high-level overviews of longer documents — distilling reports, proposals, research papers…
self-supervised learning
Exemplar learning is a self-supervised learning approach that trains models to distinguish between different transformed versions of the same image treating each image as its own…
self-supervised learning
Exemplar learning is the early self-supervised approach that groups multiple augmentations of the same image into one pseudo-class to learn invariant features - it predated…
continual learning
Exemplar selection is the process of choosing which specific examples to store in a limited memory buffer for continual learning. Since buffer space is constrained, selecting…
substrate
Exfoliation is the process of peeling or splitting thin layers from a bulk crystalline material using mechanical stress, chemical etching, or ion implantation — ranging from…
exhaust abatement efficiency prediction, exhaust abatement efficiency, abatement efficiency prediction, exhaust abatement, abatement efficiency, efficiency prediction
Exhaust Abatement Efficiency Prediction with Machine Learning Introduction & Motivation Exhaust Abatement Efficiency Prediction with Machine Learning addresses a central problem…
exhaust abatement scrubber efficiency prediction, exhaust abatement scrubber, abatement scrubber efficiency, scrubber efficiency prediction, exhaust abatement
Exhaust Abatement Scrubber Efficiency Prediction with Machine Learning Introduction & Motivation Exhaust Abatement Scrubber Efficiency Prediction with Machine Learning addresses…
exhaust duct static pressure drift prediction machine learning, exhaust duct, static pressure drift, exhaust duct static pressure drift prediction, exhaust duct static
Exhaust Duct Static Pressure Drift Prediction with Machine Learning Introduction & Motivation Exhaust Duct Static Pressure Drift Prediction with Machine Learning addresses…
exhaust fan bearing wear prediction machine learning, exhaust fan, bearing wear prediction, exhaust fan bearing wear prediction, exhaust fan bearing, fan bearing wear, fan bearing
Exhaust Fan Bearing Wear Prediction with Machine Learning Introduction & Motivation Exhaust Fan Bearing Wear Prediction with Machine Learning addresses a central problem…
wet dry burn scrubber types, arsine phosphine capture, silane thermal oxidation, removal efficiency regulation, emissions permit sizing, scrubber waste stream handling
Exhaust scrubbers neutralize the toxic, corrosive, or otherwise hazardous gases that semiconductor process tools exhaust, treating that exhaust stream before it's released to…
exhaust scrubber efficiency drift prediction, exhaust scrubber efficiency, scrubber efficiency drift, efficiency drift prediction, exhaust scrubber, scrubber efficiency
Exhaust Scrubber Efficiency Drift Prediction with Machine Learning Introduction & Motivation Exhaust Scrubber Efficiency Drift Prediction with Machine Learning addresses a central…
exhaust scrubber particulate loading backpressure prediction, exhaust scrubber particulate, scrubber particulate loading, particulate loading backpressure
Exhaust Scrubber Particulate Loading Backpressure Prediction with Machine Learning Introduction & Motivation Exhaust Scrubber Particulate Loading Backpressure Prediction…
manufacturing operations
Exhaust System is the facility subsystem that removes and treats process byproducts and airborne contaminants - It is a core method in modern semiconductor facility and process…
x-risk, AI existential risk, Nick Bostrom, Toby Ord, The Precipice, Center for AI Safety statement, extinction risk from AI
An existential risk is a danger that could cause human extinction or permanently and drastically curtail humanity's long-term potential, so that no recovery to a flourishing…
exllama, efficient
EXL2 is an advanced quantization format for ExLlamaV2 that uses dynamic per-layer bit allocation to achieve optimal quality-size trade-offs for GPU inference of large language…
quantization, inference, python, fast inference
ExLlama (and its successor ExLlamaV2) is a hyper-optimized Python/C++/CUDA inference engine specifically designed for maximum speed on NVIDIA GPUs — writing custom CUDA kernels…
exoskeleton human augmentation, exoskeleton human, human augmentation
Exoskeleton Human Augmentation Introduction & Motivation Exoskeleton Human Augmentation addresses a central problem in robotics process control…
exoskeleton human interface, exoskeleton human, human interface
Exoskeleton Human Interface Introduction & Motivation Exoskeleton Human Interface addresses a central problem in wearable process control and force-feedback-fidelity prediction…
metrology
Expanded Uncertainty (U) is the combined standard uncertainty multiplied by a coverage factor to provide a confidence interval — U = k · u_c, where k is typically 2 (providing…
process
Expanding the Process Window is the deliberate engineering of wider acceptable parameter ranges — achieved through design rule relaxation, process improvements, material changes…
time series models
Expanding Window is evaluation and training scheme where the historical window grows as time progresses. - It preserves all past data so long-run information remains available…
EM algorithm, expectation maximization, Dempster Laird Rubin 1977, E-step, M-step, latent variable estimation, Baum-Welch
The expectation-maximization (EM) algorithm is an iterative method for finding maximum-likelihood or maximum a posteriori parameter estimates in models with latent (unobserved)…
expectation maximization algorithm latent variables iterative optimization, expectation maximization, algorithm latent variables, 2d elbo em gmm hmm passed
Expectation-Maximization Algorithm: Latent Variables & Iterative Optimization Introduction & Motivation EM algorithm optimizes likelihood of models with latent variables…
expectation maximization and variants, expectation maximization variants, expectation maximization, maximization variants, bic em mnist, expectation-maximization variants
Expectation-Maximization and Variants Introduction & Motivation EM algorithm maximizes likelihood for models with latent variables. Fundamental for clustering, density estimation…
expectation maximization mixture model prediction, expectation maximization mixture, maximization mixture model, mixture model prediction, expectation maximization
Expectation Maximization Mixture Model Prediction with Machine Learning Introduction & Motivation Expectation Maximization Mixture Model Prediction with Machine Learning addresses…
eot, ai safety
EOT (Expectation Over Transformation) is a technique for attacking models that use stochastic defenses (randomized preprocessing, random dropout, random resizing) — computing…
expectation propagation distributed inference prediction, expectation propagation distributed, propagation distributed inference, distributed inference prediction
Expectation Propagation Distributed Inference Prediction with Machine Learning Introduction & Motivation Expectation Propagation Distributed Inference Prediction with Machine…
expected calibration error, ece, evaluation
Expected Calibration Error (ECE) is the primary metric for evaluating the calibration quality of a probabilistic classifier, measuring the average absolute difference between…
Expected Calibration Error ECE and Reliability Diagrams Introduction & Motivation Expected Calibration Error ECE and Reliability Diagrams addresses a central problem…
Expected Error Reduction via 0-1 Loss Minimization Introduction & Motivation Expected Error Reduction via 0-1 Loss Minimization addresses a central problem in (subject) learning…
Expected Error Reduction Query Strategy Introduction & Motivation Expected Error Reduction Query Strategy addresses a central problem in (subject) learning and efficient (target)…
Expected Model Output Change EMOC Query Strategy Introduction & Motivation Expected Model Output Change EMOC Query Strategy addresses a central problem in (subject) learning…
Expected Model Change Query Strategy Introduction & Motivation Expected Model Change Query Strategy addresses a central problem in (subject) learning and efficient (target)…
Expected Reciprocal Rank ERR Cascade Evaluation Introduction & Motivation Expected Reciprocal Rank ERR Cascade Evaluation addresses a central problem in recsys systems…
Pascal and Fermat 1654, expected monetary value, unbiased estimator, linearity of expectation
Expected value is the long-run average outcome of a random variable if the underlying random process were repeated indefinitely, calculated by multiplying each possible outcome…
supply chain & logistics
Expediting is accelerated coordination actions used to recover delayed supply, production, or shipment commitments - It mitigates imminent service failure when normal lead-time…
business
Experience curve is the broader economic relationship where total cost declines with cumulative output due to scale and learning - Cost reductions come from process learning…
hindsight experience replay, reinforcement learning advanced
Hindsight Experience is goal-conditioned replay that relabels failed trajectories as successes for alternate achieved goals. - It extracts learning signal from unsuccessful…
continual learning
Experience replay is a technique from reinforcement learning — adopted for continual learning — where the model randomly samples and replays stored examples from previous…
continual learning, catastrophic forgetting, llm training, buffer replay, lifelong learning, ai
Experience replay is a continual-learning technique that reuses buffered past samples during training on new data - Replay batches interleave old and new examples so optimization…
Experience Replay Buffer Management Introduction & Motivation Experience Replay Buffer Management addresses a central problem in reinforcement policy learning…