27,049 technical terms and definitions
Deep Reinforcement Learning 1. Introduction to Deep Reinforcement Learning Deep Reinforcement Learning (Deep RL) extends classical RL by using deep neural networks to approximate…
sim to real transfer, domain randomization robot, drl robot manipulation, reinforcement learning locomotion
Deep Reinforcement Learning (DRL) for Robotics is the application of neural network-based reinforcement learning agents to robotic control tasks including manipulation…
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Deep RIE Sidewall Angle Prediction with Machine Learning Introduction & Motivation Deep RIE Sidewall Angle Prediction with Machine Learning addresses a central problem in deep…
Deep SHAP DeepLIFT Additive Attributions Introduction & Motivation Deep SHAP DeepLIFT Additive Attributions addresses a central problem in explainability interpretation and model…
deep silicon etch undercut control prediction, deep silicon etch, silicon etch undercut, etch undercut control, undercut control prediction, deep silicon, silicon etch
Deep Silicon Etch Undercut Control Prediction with Machine Learning Introduction & Motivation Deep Silicon Etch Undercut Control Prediction with Machine Learning addresses…
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Deep Space Electronics Thermal Cycling Prediction with Machine Learning Introduction & Motivation Deep Space Electronics Thermal Cycling Prediction with Machine Learning addresses…
deep submicron cmos scaling 1990s step 12 adopt chemical mechanical polishing for planarization, adopt chemical mechanical polishing for planarization
Deep Submicron CMOS Scaling (1990s): Step 12 — Adopt Chemical-Mechanical Polishing for Multilevel Planarization 1. Why Flat Matters More as Layers Stack Up A single metal…
deep submicron cmos scaling 1990s step 17 assemble the 0.25 micron node baseline, assemble the 0.25 micron node baseline
Deep Submicron CMOS Scaling (1990s): Step 17 — Assemble the 0.25-Micron Node as the Deep Submicron Baseline 1. Bringing the Series' Separate Fixes Together Into One Process Each…
deep submicron cmos scaling 1990s step 20 close the series and mark the boundary with the next era, close the series and mark the boundary with the next era
Deep Submicron CMOS Scaling (1990s): Step 20 — Close the Series and Mark the Boundary With the Next Scaling Era 1. What This Series Has Established Across twenty steps, this…
deep submicron cmos scaling 1990s step 10 confront interconnect rc delay as a design concern, confront interconnect rc delay as a design concern
Deep Submicron CMOS Scaling (1990s): Step 10 — Confront Interconnect RC Delay as a First-Order Design Concern 1. A Reversal From the 1980s Design Picture Through the 1980s, gate…
deep submicron cmos scaling 1990s step 1 confront the new scaling wall below one micron, confront the new scaling wall below one micron
Deep Submicron CMOS Scaling (1990s): Step 1 — Confront the New Scaling Wall Below One Micron 1. The Geometric Shrink Alone Stops Being Enough Through the 1980s…
deep submicron cmos scaling 1990s step 14 engineer halo pocket implants for threshold rolloff control, engineer halo pocket implants for threshold rolloff control
Deep Submicron CMOS Scaling (1990s): Step 14 — Engineer Halo/Pocket Implants for Threshold-Voltage Rolloff Control 1. A Short-Channel Effect That Deep Submicron Geometries…
deep submicron cmos scaling 1990s step 3 engineer the lightly doped drain structure, engineer the lightly doped drain structure
Deep Submicron CMOS Scaling (1990s): Step 3 — Engineer the Lightly Doped Drain (LDD) Structure 1. The Core Idea: Grade the Field Instead of Fighting It Directly A conventional…
deep submicron cmos scaling 1990s step 2 identify hot carrier injection as the new reliability limit, identify hot carrier injection as the new reliability limit
Deep Submicron CMOS Scaling (1990s): Step 2 — Identify Hot-Carrier Injection as the New Reliability Limit 1. A Mechanism the 1970s Series Already Named, Now the Dominant Concern…
deep submicron cmos scaling 1990s step 4 integrate sidewall spacers for self aligned ldd implants, integrate sidewall spacers for self aligned ldd implants
Deep Submicron CMOS Scaling (1990s): Step 4 — Integrate Sidewall Spacers for Self-Aligned LDD Implants 1. The Alignment Problem LDD Creates Step 3 described two differently doped…
deep submicron cmos scaling 1990s step 8 integrate the self aligned salicide process, integrate the self aligned salicide process
Deep Submicron CMOS Scaling (1990s): Step 8 — Integrate the Self-Aligned Salicide (SALICIDE) Process 1. Siliciding the Gate and the Diffusions Without Extra Masks Step 7…
deep submicron cmos scaling 1990s step 18 quantify what the 0.25 micron baseline delivered, quantify what the 0.25 micron baseline delivered
Deep Submicron CMOS Scaling (1990s): Step 18 — Quantify What the 0.25-Micron Baseline Delivered Over the Decade 1. From a One-Micron Starting Point to a Quarter-Micron Close This…
deep submicron cmos scaling 1990s step 16 rebalance threshold voltage against a scaled supply rail, rebalance threshold voltage against a scaled supply rail
Deep Submicron CMOS Scaling (1990s): Step 16 — Rebalance Threshold Voltage Against a Scaled Supply Rail 1. Why Threshold Voltage Cannot Simply Stay Fixed as Supply Falls…
deep submicron cmos scaling 1990s step 7 reduce sheet resistance with silicide, reduce sheet resistance with silicide
Deep Submicron CMOS Scaling (1990s): Step 7 — Reduce Polysilicon and Diffusion Sheet Resistance With Silicide 1. A Resistance Problem That Scaling Makes Worse, Not Better…
deep submicron cmos scaling 1990s step 13 replace locos with shallow trench isolation, replace locos with shallow trench isolation
Deep Submicron CMOS Scaling (1990s): Step 13 — Replace LOCOS With Shallow Trench Isolation 1. Why LOCOS Ran Out of Room The `mosfet_scaling_1970s` series' step 7 established LOCOS…
deep submicron cmos scaling 1990s step 9 scale multilevel interconnect and adopt tungsten plugs, scale multilevel interconnect and adopt tungsten plugs
Deep Submicron CMOS Scaling (1990s): Step 9 — Scale Multilevel Interconnect and Adopt Tungsten Plugs 1. More Metal Layers, a Harder Via Problem As logic density grew through deep…
deep submicron cmos scaling 1990s step 15 scale supply voltage from five volts toward lower rails, scale supply voltage from five volts toward lower rails
Deep Submicron CMOS Scaling (1990s): Step 15 — Scale Supply Voltage From Five Volts Toward 3.3 Volts and Below 1. Why 5V Persisted Longer Than Pure Scaling Theory Wanted Step 1…
deep submicron cmos scaling 1990s step 5 scale the gate oxide toward its tunneling limit, scale the gate oxide toward its tunneling limit
Deep Submicron CMOS Scaling (1990s): Step 5 — Scale the Gate Oxide Toward Its Tunneling Limit 1. Why the Oxide Has to Thin Along With Everything Else Gate oxide capacitance per…
deep submicron cmos scaling 1990s step 19 situate deep submicron cmos as foundation of the 1990s boom, situate deep submicron cmos as foundation of the 1990s boom
Deep Submicron CMOS Scaling (1990s): Step 19 — Situate Deep Submicron CMOS as the Foundation of the 1990s Microprocessor Boom 1. The Decade's Visible Story Rested on This Series'…
deep submicron cmos scaling 1990s step 6 weigh oxide thinning against reliability and leakage, weigh oxide thinning against reliability and leakage
Deep Submicron CMOS Scaling (1990s): Step 6 — Weigh Oxide Thinning Against Reliability and Leakage Tradeoffs 1. Combining Two Independent Field-Driven Risks Steps 2 through 4…
deep submicron cmos scaling 1990s step 11 weigh motivations for moving toward copper, weigh motivations for moving toward copper
Deep Submicron CMOS Scaling (1990s): Step 11 — Weigh the Motivations for Moving Beyond Aluminum Toward Copper 1. Aluminum's Two Compounding Weaknesses at Deep Submicron Dimensions…
Deep Kernel SVDD for Nonlinear Boundary Formulation Introduction & Motivation Deep Kernel SVDD for Nonlinear Boundary Formulation addresses a central problem in anomaly detection…
Deep Support Vector Data Description Deep SVDD Introduction & Motivation Deep Support Vector Data Description Deep SVDD addresses a central problem in anomaly detection…
Deep Support Vector Data Description Introduction & Motivation Deep Support Vector Data Description addresses a central problem in anomaly detection and {target} scoring: how…
Deep Taylor Decomposition Introduction & Motivation Deep Taylor Decomposition addresses a central problem in interpretability and explanation of xai predictions: how to Produce…
Deep Taylor Decomposition (Extended Study) Introduction & Motivation Deep Taylor Decomposition (Extended Study) addresses a central problem in interpretability and explanation…
Deep Taylor Decomposition (Extended Study) Introduction & Motivation Deep Taylor Decomposition (Extended Study) addresses a central problem in interpretability and explanation…
Deep Transfer Clustering for Novel Category Discovery Introduction & Motivation Deep Transfer Clustering for Novel Category Discovery addresses a central problem in transfer…
signal & power integrity, bdtc, trench capacitor, pdn
Power Distribution Networks and on-chip power grid architectures constitute the physical and electrical infrastructure engineered to deliver stable supply voltages and ground…
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Deep UV Scanner Pulse Energy Stability Prediction with Machine Learning Introduction & Motivation Deep UV Scanner Pulse Energy Stability Prediction with Machine Learning addresses…
robotics
Deep visual odometry is the data-driven approach that estimates camera motion between frames using neural networks instead of purely handcrafted geometric pipelines - it can…
computer vision
Deep ViT training is the set of optimization practices required to keep very deep vision transformers stable, diverse, and performant over long training runs - as depth increases…
audio & speech
Deep Voice is a neural text-to-speech pipeline replacing traditional handcrafted TTS components. - It introduced end-to-end trainable neural modules for major stages of production…
audio & speech
Deep Voice 2 is a multi-speaker neural TTS system conditioned on learnable speaker embeddings. - It supports many voices in one model and enables efficient adaptation to new…
audio & speech
Deep Voice 3 is a fully convolutional neural text-to-speech architecture for fast parallelizable synthesis. - It removes recurrent bottlenecks to improve throughput during…
Deep Walk Graph Embedding Introduction & Motivation Deep Walk Graph Embedding addresses a central problem in graph network representation and random-walk-coverage learning: how…
time series models
DeepAR is an autoregressive probabilistic forecasting model that predicts future distributions using recurrent networks - The model conditions on past observations and covariates…
DeepAR Recurrent Probabilistic Forecasting Introduction & Motivation DeepAR Recurrent Probabilistic Forecasting addresses a central problem in timeseries forecasting…
unit test, evaluation, metrics
DeepEval is an open-source LLM evaluation framework that runs as pytest-compatible unit tests in CI/CD pipelines — providing pre-built metrics for hallucination detection…
synthetic, detection
Deepfakes are AI-generated synthetic videos that realistically swap faces or manipulate expressions using deep learning. Detection is an ongoing arms race as generation techniques…
computer vision
Deepfake detection uses computer vision and deep learning to identify AI-generated or manipulated media, including face-swapped videos, synthetic audio, and altered images. As…
ai generated image detection, synthetic media forensics, face forgery detection
Deepfake Detection is the set of AI and forensic techniques used to identify synthetically generated or manipulated images, videos, and audio — analyzing artifacts in frequency…
recommendation systems
DeepFM is a recommendation architecture that jointly learns low-order feature interactions and high-order deep patterns - A factorization-machine component and deep network share…
ai safety
DeepFool is an adversarial attack that finds the minimum perturbation needed to cross the decision boundary — iteratively linearizing the decision boundary and computing…
interpretability
DeepFool is an iterative attack that approximates decision boundaries to find near-minimal adversarial perturbations - It estimates the smallest input change needed to cross…