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Glossary

1,821 technical terms and definitions

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database-replication-consistency machine learning

database replication consistency, database replication, replication consistency

Database Replication Consistency Introduction & Motivation Database Replication Consistency addresses a central problem in cloud process control…

database-sharding-strategy machine learning

database sharding strategy, database sharding, sharding strategy

Database Sharding Strategy Introduction & Motivation Database Sharding Strategy addresses a central problem in cloud process control and shard-key-distribution-balance prediction…

databases

data modeling, relational tables, SQL, primary key, index, transaction, ACID

A database is the organized, persistent storage sitting behind the HTTP response from the previous topic, and this very article is a real row inside one. Data modeling is…

databricks

lakehouse, mlflow

Databricks is the unified data intelligence platform founded by the creators of Apache Spark that combines data engineering, data warehousing, and machine learning — pioneering…

dataflow

architecture, deep, learning, processing

Dataflow is an execution model in which a computation is expressed as a graph of operations, and each operation runs as soon as its input data is available, rather than in a fixed…

dataflow

computing, paradigm, architecture, execution

Dataflow Computing Paradigm is an execution model where computation is driven by data availability rather than program counter sequencing, enabling massive parallelism through…

dataflow-accelerator-design machine learning

dataflow accelerator architecture design, dataflow accelerator architecture, accelerator architecture design, dataflow accelerator, accelerator architecture, architecture design

Dataflow Accelerator Architecture Design Introduction & Motivation Dataflow Accelerator Architecture Design addresses a central problem in ml-hardware process control…

dataflow-analysis-compiler machine learning

dataflow analysis in compilers, dataflow analysis compilers, dataflow analysis, analysis compilers

Dataflow Analysis in Compilers Introduction & Motivation Dataflow Analysis in Compilers addresses a central problem in compiler-optimization process control and analysis-precision…

dataflow architecture

dataflow programming, dataflow graph, stream processing, dataflow execution

Dataflow architecture definition and engineering boundary. executes an operation when its required data tokens are available rather than advancing primarily through a single…

dataflow architecture computing

spatial computing hardware, coarse grain reconfigurable, cgra dataflow, dataflow processor design

Dataflow Architecture Computing is the processor design paradigm where instructions execute as soon as their input operands are available (data-driven execution) rather than…

dataflow-computing-architecture machine learning

dataflow computing and architectures, dataflow computing architectures, dataflow computing, computing architectures

Dataflow Computing and Architectures Introduction & Motivation Dataflow Computing and Architectures addresses a central problem in dataflow-computing process control…

dataflow processor architecture

wave computing, spatial architecture computing, coarse grain reconfigurable array cgra, stream dataflow architecture

Dataflow Processor Architecture: Spatial Computing via Coarse-Grained Reconfigurable Arrays — compute elements directly mapped to hardware nodes with data-driven execution model…

dataset

corpus, training data

Training Data for LLMs Pretraining Datasets Large language models are pretrained on massive text corpora—often trillions of tokens from diverse sources. Common Pretraining Sources…

dataset bias

data quality

Dataset Bias refers to systematic errors or skews in training data that cause models to learn unintended, misleading patterns — the model captures the bias in the data rather than…

dataset sharding

distributed training

Dataset sharding is the partitioning of training data into non-overlapping subsets assigned across distributed workers - it ensures balanced workload distribution, minimizes…

dataset versioning

mlops

Dataset versioning is the practice of creating immutable, traceable dataset snapshots for every training and evaluation run - it ensures model results can be reproduced even when…

datasets

huggingface, loading

Hugging Face Datasets is a lightweight Python library for efficiently loading, processing, and sharing datasets for machine learning — using Apache Arrow as its in-memory backend…

datasheet

dataset, documentation

Datasheets for Datasets is the standardized documentation framework for machine learning training datasets that captures motivation, composition, collection process…

date code

packaging

Date code is the encoded manufacturing-time identifier printed or marked on packages to indicate production period for traceability - it supports quality control, inventory…

date-time-parsing machine learning

Date-Time Parsing and Normalization Introduction & Motivation Date-Time Parsing and Normalization addresses a central problem in {subject} assurance and {target} management: how…

date understanding

evaluation

Date Understanding is the NLP task and benchmark category that evaluates a model's ability to reason about temporal expressions, calendar arithmetic, event ordering, and duration…

day-to-day variation

d2d variation, daily drift

Day-to-Day Variation (D2D) in semiconductor manufacturing refers to process parameter fluctuations between production days caused by environmental, equipment, or operational…

dbn

dbn, recommendation systems

DBN is dynamic Bayesian network click model that captures sequential examination and satisfaction behavior - It extends simpler click models with richer latent user-state…

dbr mirror layer thickness drift prediction machine learning

dbr mirror layer thickness drift prediction, dbr mirror layer, mirror layer thickness, layer thickness drift, thickness drift prediction, dbr mirror, mirror layer

DBR Mirror Layer Thickness Drift Prediction with Machine Learning Introduction & Motivation DBR Mirror Layer Thickness Drift Prediction with Machine Learning addresses a central…

dbr mirror stack uniformity prediction with machine learning

dbr mirror stack uniformity prediction machine learning, dbr mirror, stack uniformity prediction, dbr vcsel, dbr mirror stack uniformity prediction, dbr mirror stack

DBR Mirror Stack Uniformity Prediction with Machine Learning Introduction & Motivation DBR Mirror Stack Uniformity Prediction with Machine Learning addresses a central problem…

dbscan

dbscan, manufacturing operations

DBSCAN is a density-based clustering algorithm that groups dense regions while labeling sparse points as noise - It is a core method in modern semiconductor predictive analytics…

dbscan-clustering-anomaly machine learning

dbscan clustering anomaly, dbscan clustering, clustering anomaly

DBSCAN Clustering Anomaly Introduction & Motivation DBSCAN Clustering Anomaly addresses a central problem in timeseries process control and density-based-clustering-method…

dbscan density-based clustering arbitrary shapes

dbscan density based clustering arbitrary shapes, dbscan density clustering arbitrary shapes, dbscan density, clustering arbitrary shapes, dbscan gps gpu passed, dbscan density-based

DBSCAN: Density-Based Clustering & Arbitrary Shapes Introduction & Motivation DBSCAN groups points based on density—clusters are dense regions separated by sparse regions. No need…

dbscan density-based clustering spatial analysis

dbscan density based clustering spatial analysis, dbscan density clustering spatial analysis, dbscan density, clustering spatial analysis, 2d dbscan knn nn passed, dbscan density-based

DBSCAN: Density-Based Clustering & Spatial Analysis Introduction & Motivation DBSCAN: density-based spatial clustering. No fixed K; finds clusters of arbitrary shape. Noise points…

dbt

transform, analytics

dbt (Data Build Tool) is the SQL-first transformation framework that brings software engineering best practices — version control, testing, documentation, and modular design…

dc-hw machine learning

data center hardware, data center, center hardware

Data Center Hardware Introduction & Motivation Data Center Hardware addresses a central problem in data-center process control and pue prediction: how to Predict pue from dc-hw…

dc parametric

dc, advanced test & probe

DC Parametric is direct-current electrical measurements used to verify static device behavior against limits - It validates leakage, threshold, drive, and other core electrical…

dc sputtering

direct current sputtering, dc magnetron sputtering, dc sputter deposition, dc sputtering power, dc sputtering voltage, dc sputtering current, dc sputtering arcing, pulsed dc sputtering, reactive dc sputtering, dc plasma impedance, arc suppression

DC sputtering is the electrical operating regime in which a negative direct-current supply sustains a glow discharge at a conductive cathode target, converting a controlled…

dc testing

testing

Design-for-test architectures, automatic test pattern generation, and structural fault modeling constitute the digital verification and manufacturing test disciplines engineered…

ddim (denoising diffusion implicit models)

ddim, denoising diffusion implicit models, generative models

DDIM (Denoising Diffusion Implicit Models) is an accelerated sampling method for diffusion models that defines a family of non-Markovian diffusion processes sharing the same…

ddim sampling

ddim, generative models

DDIM sampling is the non-Markov diffusion sampling method that enables deterministic or partially stochastic generation with fewer steps - it reuses DDPM-trained models while…

double diffused mesa transistor select and prepare starting silicon wafer

ddmesa 1956 step 1 choose collector resistivity and crystal orientation, starting silicon wafer resistivity selection

Select and Prepare the Starting Silicon Wafer: Choosing the Crystal That Will Carry a Field It Doesn't Yet Have 1. Why Silicon's Resistivity Has to Be Chosen Before Anything Else…

double diffused mesa transistor boron base predeposition step

ddmesa 1956 step 3 boron predeposition for graded base, boron base predeposition

Base Predeposition: Laying Down the Boron That Will Become a Graded Base, Not Yet a Flat One 1. Why Predeposition Is Deliberately Separated From Drive-In This step introduces…

double diffused mesa transistor base drive in graded doping profile

ddmesa 1956 step 4 drive in diffusion builds drift field, base drive in graded profile

Base Drive-In: Stretching a Fixed Dose Into the Gradient That Becomes a Built-In Field 1. Why Drive-In Is the Step That Actually Creates the Drift Field This step takes the fixed…

double diffused mesa transistor verify base drift field profile

ddmesa 1956 step 5 measure the graded base doping gradient, verify base drift field profile

Verify the Base Drift-Field Profile: Measuring a Gradient Before Trusting It With an Emitter 1. Why a Field You Cannot Measure Is a Field You Cannot Rely On Before any emitter…

double diffused mesa transistor phosphorus emitter predeposition

ddmesa 1956 step 6 second diffusion opposite polarity dopant, emitter predeposition phosphorus

Emitter Predeposition: A Second Dopant, Opposite Polarity, Placed Inside a Field That Is Already Waiting For It 1. Why the Emitter Must Be Shallower and More Heavily Doped Than…

double diffused mesa transistor emitter drive in shallow junction

ddmesa 1956 step 7 fix emitter junction depth and gradient, emitter drive in shallow junction

Emitter Drive-In: Fixing the Junction Depth Without Letting It Catch Up to the Base 1. Why This Drive-In Has the Opposite Goal From the Base's This step anneals the phosphorus…

double diffused mesa transistor mask the active device area

ddmesa 1956 step 8 photoresist mask before mesa etch, mask active device area

Mask the Active Device Area: Deciding What the Next Step Is Allowed to Destroy 1. Why a Mask Has to Exist Before an Etch Can Be Trusted This step applies a protective dot — wax…

double diffused mesa transistor mesa etch isolates each junction

ddmesa 1956 step 9 etch through base and emitter junctions, mesa etch junction isolation

Mesa Etch: Cutting Through Two Junctions to Leave Each Device Standing Alone 1. Why the Etch Has to Go Deeper Than Either Junction, Not Just Between Them This step exposes…

double diffused mesa transistor base ohmic contact metallization

ddmesa 1956 step 12 metal contact on graded base region, base ohmic contact metallization

Base Ohmic Contact Metallization: Attaching a Wire to a Region That Is Not Flat Electrically 1. Why a Contact to a Graded Region Needs More Care Than a Contact to a Uniform One…