1,686 technical terms and definitions
dft, design for testability, scan chain, atpg, bist, jtag
Design-for-test architectures, automatic test pattern generation, and structural fault modeling constitute the digital verification and manufacturing test disciplines engineered…
3d vision
D-NeRF is the dynamic extension of Neural Radiance Fields that models non-rigid scene motion by learning deformations from each time step into a canonical 3D space - it enables…
doe
D-Optimal Design is the most widely used optimal experimental design criterion — selecting the set of experimental runs that maximizes the determinant of the information matrix (…
audio & speech
D-vector is a neural speaker representation produced by sequence encoders for speaker characterization - Frame-level features are aggregated into utterance-level vectors used…
d2d, die-to-die variation, manufacturing
D2D (Die-to-Die Variation) Overview Die-to-die variation describes systematic parameter differences between dies at different locations on the same wafer. D2D variation is largely…
digital to analog converter, dac architecture, data converter
DAC is a digital-to-analog converter that reconstructs a voltage, current, charge, time interval, or waveform from numerical codes. DACs drive audio, wireline and RF transmitters…
digital analog converter, dac architecture, current steering dac, sigma delta dac
DAC (Digital-to-Analog Converter) Design is the art of converting digital binary codes into precise analog voltages or currents — a fundamental mixed-signal building block used…
dac differential nonlinearity prediction, dac differential nonlinearity, differential nonlinearity prediction, dac differential, differential nonlinearity
DAC Differential Nonlinearity Prediction with Machine Learning Introduction & Motivation DAC Differential Nonlinearity Prediction with Machine Learning addresses a central problem…
dac integral nonlinearity prediction, dac integral nonlinearity, integral nonlinearity prediction, dac integral, integral nonlinearity, nonlinearity prediction
DAC Integral Nonlinearity Prediction with Machine Learning Introduction & Motivation DAC Integral Nonlinearity Prediction with Machine Learning addresses a central problem in DAC…
dac offset drift test prediction, dac offset drift, offset drift test, drift test prediction, dac offset, offset drift, drift test, test prediction
DAC Offset Drift Test Prediction with Machine Learning Introduction & Motivation DAC Offset Drift Test Prediction with Machine Learning addresses a central problem…
dac output settling time drift prediction machine learning, dac output, settling time drift, dac, dac output settling time drift prediction, dac output settling, settling time
DAC Output Settling Time Drift Prediction with Machine Learning Introduction & Motivation DAC Output Settling Time Drift Prediction with Machine Learning addresses a central…
daemon set node coverage, daemon set node, set node coverage, daemon set, set node, node coverage
Daemon Set Node Coverage Introduction & Motivation Daemon Set Node Coverage addresses a central problem in cloud process control and node-affinity-tolerations prediction: how…
imitation learning
DAgger (Dataset Aggregation) is an imitation learning algorithm that addresses behavioral cloning's distribution shift problem — iteratively collecting new expert labels for…
Deep Autoencoding Gaussian Mixture Model DAGMM Introduction & Motivation Deep Autoencoding Gaussian Mixture Model DAGMM addresses a central problem in anomaly detection…
data assets, orchestration
Dagster is the asset-centric data orchestration platform that models data pipelines as software-defined assets rather than imperative tasks — enabling data engineering teams…
dall-e, multimodal ai
DALL-E 3 is an advanced text-to-image generation model with stronger prompt understanding and composition - It improves semantic faithfulness and fine-grained scene rendering.…
dall e text to image generation, dall e text image generation, dall e, text image generation, clip coco dall fid ms, dall-e text-to-image generation, dall-e text-to-image, text-to-image generation
DALL-E - Text-to-Image Generation Introduction & Motivation DALL-E: generate images from text descriptions. Vision and language understanding. Applications: creative tools…
dall-e, multimodal ai
DALL-E Tokenizer is a learned image tokenizer that converts visual content into discrete code tokens - It enables image generation as a sequence modeling problem. What Is DALL-E…
colma, serramonte
Daly City is city intent for Daly City and nearby subregion references such as Colma and Serramonte - It is a core method in modern semiconductor AI, geographic-intent routing…
damascene, dual-damascene, dual damascene, single damascene, copper damascene, damascene inlay, damascene integration, damascene interconnect, damascene metallization, copper inlay process, via-first, trench-first, semi-damascene
The damascene process exists because copper cannot be plasma-etched. Aluminium forms volatile AlCl₃ at room temperature in a chlorine plasma, so for decades the interconnect…
dual damascene, copper damascene, inlaid metallization, copper interconnect
Copper dual damascene interconnect architectures, electrochemical superfilling, and barrier-seed metallization constitute the back-end-of-line (BEOL) wiring systems that route…
interconnect controlling line width in damascene interconnect processes, interconnect controlling line, controlling line width, line width damascene, width damascene interconnect
Interconnect: Controlling line width in damascene interconnect processes Introduction & Motivation Interconnect: Controlling line width in damascene interconnect processes…
damascene process line width control, damascene process line, process line width, line width control, damascene process, process line, line width, width control
Damascene Process Line Width Control with Machine Learning Introduction & Motivation Damascene Process Line Width Control with Machine Learning addresses a central problem…
dan, do anything now, ai safety
DAN (Do Anything Now) is the most widely known jailbreak prompt framework that attempts to make ChatGPT bypass its safety restrictions by role-playing as an unrestricted AI…
jailbreak, llm safety, adversarial prompts, prompt injection, ai safety, alignment, ai security
DAN prompts are jailbreaking techniques that attempt to bypass AI safety guardrails by instructing the model to role-play as "Do Anything Now" — adversarial prompts that frame…
dann, domain adaptation
DANN (Domain-Adversarial Neural Network) is the seminal, groundbreaking architecture defining modern Deep Domain Adaptation, mathematically forcing a feature extractor to learn…
dare, model merging
DARE (Drop and Rescale) is a model merging technique that randomly drops (zeros out) a fraction of fine-tuned parameter changes and rescales the remaining ones — reducing…
dark field bright field inspection correlation prediction with machine learning, dark field bright field inspection correlation prediction machine learning
Dark-Field/Bright-Field Inspection Correlation Prediction with Machine Learning Introduction & Motivation Dark-Field/Bright-Field Inspection Correlation Prediction with Machine…
dark field inspection sensitivity drift prediction machine learning, dark field, inspection sensitivity drift, dark field inspection sensitivity drift prediction
Dark Field Inspection Sensitivity Drift Prediction with Machine Learning Introduction & Motivation Dark Field Inspection Sensitivity Drift Prediction with Machine Learning…
model compression
Dark Knowledge is the rich information contained in a teacher model's soft output distribution — the relative probabilities assigned to incorrect classes reveal the model's…
model optimization
Dark Knowledge is informative class-probability structure in teacher outputs that reveals inter-class relationships - It captures nuanced uncertainty patterns not present in hard…
power wall, utilization wall, power density, dennard scaling end, thermal ceiling, utilization ceiling, dark silicon problem
Dark silicon is the fraction of a chip that must be left unpowered — kept dark — at any given moment because the whole die cannot be switched on within its power and thermal…
dark silicon active core percentage, dark silicon active, silicon active core, active core percentage, dark silicon, silicon active, active core, core percentage
Dark Silicon Active Core Percentage Introduction & Motivation Dark Silicon Active Core Percentage addresses a central problem in design process control…
dark silicon and active core percentage, dark silicon active, silicon active core, active core percentage, dark silicon, silicon active, active core, core percentage
Dark Silicon and Active Core Percentage Introduction & Motivation Dark Silicon and Active Core Percentage addresses a central problem in design process control…
dark silicon percentage, dark silicon, silicon percentage
Dark Silicon Percentage Introduction & Motivation Dark Silicon Percentage addresses a central problem in design process control and active-core prediction: how to Predict…
dark silicon management, dark silicon, silicon management
Dark Silicon Management Introduction & Motivation Dark Silicon Management addresses a central problem in design process control and core-pct prediction: how to Predict core-pct…
dark silicon and active core percentage, dark silicon active, silicon active core, active core percentage, dark silicon, silicon active, active core, core percentage
Dark Silicon and Active Core Percentage Introduction & Motivation Dark Silicon and Active Core Percentage addresses a central problem in design process control…
metrology
Darkfield Inspection is a semiconductor metrology technique that illuminates wafers at oblique angles and collects only scattered light from defects — blocking the specular…
darts, neural architecture search
DARTS is a differentiable neural-architecture-search method that relaxes discrete architecture choices into continuous optimization - Architecture parameters and network weights…
DARTS Differentiable Architecture Search Introduction & Motivation DARTS Differentiable Architecture Search addresses a central problem in automated design of nas architectures…
DARTS Differentiable Architecture Search (Extended Study) Introduction & Motivation DARTS Differentiable Architecture Search (Extended Study) addresses a central problem…
DARTS Differentiable Architecture Search (Extended Study) Introduction & Motivation DARTS Differentiable Architecture Search (Extended Study) addresses a central problem…
DARTS Differentiable Architecture Search (Extended Study) Introduction & Motivation DARTS Differentiable Architecture Search (Extended Study) addresses a central problem…
parallel, distributed
Dask is the parallel computing library for Python that scales NumPy, Pandas, and Scikit-Learn workflows from a single workstation to a cluster by chunking data into manageable…
Python, parallel, computing, distributed, task, scheduler, lazy
Dask Python Parallel Computing is a flexible Python library providing parallel computing via task graphs and lazy evaluation, enabling scalable data processing on single machines…
dask array dataframe, dask scheduler, dask delayed computation, dask distributed cluster, dask vs spark, dask task graph, dask ml, dask chunk size, dask out of core
Dask scales Python's data stack—NumPy, Pandas, scikit-learn—beyond single-machine memory by representing a computation as a directed acyclic task graph that a scheduler executes…
parallelism, all-reduce, optimization, algorithms
Data Parallelism All-Reduce Optimization is a distributed training methodology replicating models across devices, computing gradients independently, and aggregating through…
data
Data Annotation is the process of labeling raw data with meaningful tags, categories, or metadata to create training datasets for supervised machine learning — encompassing text…
privacy
Data anonymization is the process of removing or modifying personally identifiable information (PII) from datasets so that individuals cannot be identified from the remaining…
training techniques
Data Anonymization is process that irreversibly removes identifying information so individuals cannot be reasonably reidentified - It is a core method in modern semiconductor AI…