27,049 technical terms and definitions
borophosphosilicate glass, phosphosilicate glass, reflow dielectric, bpsg psg dielectric
BPSG and PSG Dielectrics are the doped silicon oxide insulating films used as interlayer dielectrics and planarization layers in CMOS fabrication — where the addition of boron…
ion implantation, p type, n type, boron, phosphorus, acceptor, donor, n-type, p-type
Doping is how a piece of silicon is turned into a device. Pure, intrinsic silicon is a mediocre conductor with very few free carriers, and on its own it can do nothing useful.…
ion implantation, p type, n type, boron, phosphorus
Doping is how a piece of silicon is turned into a device. Pure, intrinsic silicon is a mediocre conductor with very few free carriers, and on its own it can do nothing useful.…
simulation
Doping Profile Simulation models dopant distribution resulting from ion implantation and thermal diffusion — predicting 1D/2D/3D dopant concentration profiles that determine…
n-type doping, p-type doping, dopant
Doping semiconductor is the controlled introduction of impurity atoms into a nearly pure crystal so engineers can set its electrical behavior. In silicon, this step is…
healthcare ai
Dosage Extraction is the clinical NLP subtask of identifying and parsing numeric dosage information — amounts, units, routes, frequencies, and dosing schedules…
dose mapper correction accuracy drift, dose mapper correction, mapper correction accuracy, correction accuracy drift, dose mapper, mapper correction, accuracy drift
Dose Mapper Correction Accuracy Drift with Machine Learning Introduction & Motivation Dose Mapper Correction Accuracy Drift with Machine Learning addresses a central problem…
dose mapping correction accuracy prediction machine learning, dose mapping, correction accuracy prediction, cd, dose mapping correction accuracy prediction
Dose Mapping Correction Accuracy Prediction with Machine Learning Introduction & Motivation Dose Mapping Correction Accuracy Prediction with Machine Learning addresses a central…
quality & reliability
Dot Plot is a pointwise chart that displays each individual observation without aggregation into bins - It is a core method in modern semiconductor statistical analysis…
vector db
Dot product similarity measures vector similarity as their inner product, fundamental to attention and retrieval. Formula: A dot B = sum(a_i * b_i). Unbounded range.…
training phenomena
Double descent is the phenomenon where test error follows a non-monotonic curve as model complexity increases—first decreasing (classical regime), then increasing (interpolation…
double diffused mesa transistor 1956 bell labs rca, drift field graded base silicon mesa transistor, shockley semiconductor nobel prize 1956 silicon transistor
The Double-Diffused Silicon Mesa Transistor: A Graded Base That Lets an Electric Field Do What Diffusion Alone Could Not In 1956, Bell Labs and RCA moved the double-diffusion…
reinforcement learning
Double DQN is an improvement to DQN that addresses the overestimation bias in Q-learning — using the online network to select the best action and the target network to evaluate…
Double DQN Introduction & Motivation Double DQN addresses a central problem in sequential decision making via deeprl: how to Learn a policy that maximizes cumulative reward…
Double DQN (Extended Study) Introduction & Motivation Double DQN (Extended Study) addresses a central problem in sequential decision making via deeprl: how to Learn a policy that…
Double DQN (Extended Study) Introduction & Motivation Double DQN (Extended Study) addresses a central problem in sequential decision making via deeprl: how to Learn a policy that…
Double DQN (Extended Study) Introduction & Motivation Double DQN (Extended Study) addresses a central problem in sequential decision making via deeprl: how to Learn a policy that…
double patterning overlay accuracy budget prediction, double patterning overlay, patterning overlay accuracy, overlay accuracy budget, accuracy budget prediction, double patterning
Double Patterning Overlay Accuracy Budget Prediction with Machine Learning Introduction & Motivation Double Patterning Overlay Accuracy Budget Prediction with Machine Learning…
double patterning overlay accuracy prediction, double patterning overlay, patterning overlay accuracy, overlay accuracy prediction, double patterning, patterning overlay
Double Patterning Overlay Accuracy Prediction with Machine Learning Introduction & Motivation Double Patterning Overlay Accuracy Prediction with Machine Learning addresses…
double patterning overlay error prediction machine learning, double patterning, overlay error prediction, cd, double patterning overlay error prediction, patterning overlay
Double Patterning Overlay Error Prediction with Machine Learning Introduction & Motivation Double Patterning Overlay Error Prediction with Machine Learning addresses a central…
Double Q-Learning Stabilization Introduction & Motivation Double Q-Learning Stabilization addresses a central problem in reinforcement policy learning and overestimation-reduction…
quality & reliability
Double Sampling is a two-stage sampling approach that allows early accept or reject decisions before full inspection - It reduces average inspection load when process quality is…
double side polish thickness uniformity prediction with machine learning, double side polish thickness uniformity prediction machine learning, double side
Double-Side Polish Thickness Uniformity Prediction with Machine Learning Introduction & Motivation Double-Side Polish Thickness Uniformity Prediction with Machine Learning…
double-sided cooling power module prediction, double-sided cooling power, cooling power module, power module prediction, double-sided cooling, cooling power, power module
Double-Sided Cooling Power Module Prediction with Machine Learning Introduction & Motivation Double-Sided Cooling Power Module Prediction with Machine Learning addresses a central…
double spending prevention, double spending, spending prevention
Double Spending Prevention Introduction & Motivation Double Spending Prevention addresses a central problem in blockchain process control and confirmation-requirement-depth…
Augmented Inverse Probability Weighting AIPW Introduction & Motivation Augmented Inverse Probability Weighting AIPW addresses a central problem in causal inference and {target}…
Doubly Robust Estimation Introduction & Motivation Doubly Robust Estimation addresses a central problem in estimating causal effects with causalml methods: how to Estimate…
Doubly Robust Causal Estimation Introduction & Motivation Doubly Robust Causal Estimation addresses a central problem in causal inference and {target} analysis: how to Apply…
Doubly Robust Estimation (Extended Study) Introduction & Motivation Doubly Robust Estimation (Extended Study) addresses a central problem in estimating causal effects…
Doubly Robust Estimation (Extended Study) Introduction & Motivation Doubly Robust Estimation (Extended Study) addresses a central problem in estimating causal effects…
Doubly Robust Estimation (Extended Study) Introduction & Motivation Doubly Robust Estimation (Extended Study) addresses a central problem in estimating causal effects…
recommendation systems
Doubly Robust Rec is off-policy estimation combining direct outcome models with propensity correction for robustness. - It reduces bias if either the reward model or propensity…
cmp down force, polishing pressure, cmp contact pressure, preston equation, preston law, removal rate cmp, multi zone carrier, cmp
CMP removal rate is the thickness or mass of target thin-film material polished from a semiconductor wafer per unit time during chemical mechanical planarization, traditionally…
class imbalance, undersampling
Down-sampling is reducing the frequency of overrepresented classes or domains to improve training balance - It limits dominance from high-volume sources that would otherwise crowd…
downstream asher strip rate uniformity prediction machine learning, downstream asher, strip rate uniformity, downstream asher strip rate uniformity prediction
Downstream Asher Strip Rate Uniformity Prediction with Machine Learning Introduction & Motivation Downstream Asher Strip Rate Uniformity Prediction with Machine Learning addresses…
downstream plasma asher chamber wall deposit prediction machine learning, downstream plasma, asher chamber wall, downstream plasma asher, plasma asher chamber
Downstream Plasma Asher Chamber Wall Deposit Prediction with Machine Learning Introduction & Motivation Downstream Plasma Asher Chamber Wall Deposit Prediction with Machine…
transfer learning
Downstream Task is the target task that a pre-trained model is applied to after self-supervised or supervised pre-training — used to evaluate the quality of learned…
production
Downtime is time when a tool is not available for production due to failures, maintenance, or other issues, directly impacting fab capacity and output. Downtime categories: (1)…
production
Downtime analysis is the structured investigation of tool stoppage events to quantify loss drivers and identify highest-return corrective actions - it converts raw outage logs…
dp-sgd, training techniques
DP-SGD is differentially private stochastic gradient descent that clips per-example gradients and adds calibrated noise - It is a core method in modern semiconductor AI serving…
dp-sgd, differentially private sgd, privacy
DP-SGD (Differentially Private Stochastic Gradient Descent) is the foundational algorithm for training machine learning models with formal differential privacy guarantees…
diffusion sampler, stable diffusion
DPM++ (Diffusion Probabilistic Model++) is an advanced sampling method for diffusion models — generating high-quality images in fewer steps than DDPM through improved ODE solvers…
generative models
DPM-Solver is a family of high-order ODE solvers specifically designed for the probability flow ODE of diffusion models, providing faster and more accurate sampling than generic…
generative models
DPM-Solver is the family of high-order numerical solvers for diffusion ODEs that attains strong quality with very few model evaluations - it is one of the most effective…
defects per million, quality metric
DPMO (Defects Per Million Opportunities) is the universal, normalized quality metric used across the global semiconductor, automotive, aerospace, and manufacturing industries…
dpmo, quality & reliability
DPMO is defects per million opportunities, a normalized metric expressing defect frequency relative to total opportunities - It enables cross-process comparison of quality…
direct preference, simpler
Direct Preference Optimization (DPO) is the fine-tuning algorithm that aligns language models with human preferences without requiring a separate reward model or reinforcement…
dpp, recommendation systems
DPP Rec is determinantal point process based recommendation for diversity-aware subset selection. - It models item-set probability so high-quality but mutually dissimilar items…
dppm, quality
DPPM (Defective Parts Per Million) is the primary quality metric measuring the rate of defective devices shipped to customers — calculated as , representing the outgoing quality…
dppm, yield enhancement
DPPM is defective parts per million, a quality metric that quantifies escaped defect rate in shipped units - DPPM normalizes field or outgoing defects by shipped volume to track…