15,404 technical terms and definitions
mixed precision training explained, ai training precision meaning, mixed precision ai model, what does mixed precision mean
Mixed precision training is a technique for training AI models that uses lower-precision numbers for most calculations to gain speed and memory savings, while selectively keeping…
moe llm architecture explained, sparse expert routing, moe vs dense model, expert router transformer, moe memory capacity accelerator
Mixture-of-experts (MoE) is an architecture where a model has far more total parameters than a comparably-sized dense model, but only activates a small fraction of them for any…
ai model distillation explained, knowledge distillation meaning, teacher student model ai, what does model distillation mean
Model distillation is a technique for training a smaller, faster "student" AI model to mimic the behavior of a larger, more capable "teacher" model, transferring much of…
model quantization explained, ai model quantization meaning, quantization neural network, why quantize ai models
Model quantization is a technique that shrinks an AI model by representing its internal numbers with less precision — for example, using 8-bit values instead of the 32-bit values…
moore's law explained, moores law meaning, is moores law dead, moores law transistor count
Moore's Law is the observation, made by Intel co-founder Gordon Moore in 1965, that the number of transistors that can be economically packed onto a chip roughly doubles every…
non-recurring engineering cost, chip nre explained, semiconductor nre meaning, nre vs per-unit cost
Non-recurring engineering cost, or NRE cost, refers to the one-time upfront expense of designing and preparing a chip for manufacturing — covering things like design work…
opc lithography explained, optical proximity correction meaning, computational lithography opc, what does opc mean in chip manufacturing
Optical proximity correction, or OPC, is a computational technique that deliberately distorts the pattern on a photomask in precise, calculated ways so that after light…
photoresist explained, photoresist lithography meaning, positive vs negative photoresist, how does photoresist work
Photoresist is a light-sensitive chemical coating applied to a silicon wafer before lithography, one that chemically changes when exposed to light so that, after development, it…
plasma etching explained, RIE vs ICP etch, anisotropic etch semiconductor, dry etch process chip fabrication, plasma etch selectivity endpoint
Plasma etch is how a chip factory carves the pattern from a photomask into the actual silicon, layer by layer. After lithography exposes a resist pattern on the wafer, that…
poly gate etch explained, gate critical dimension control, polysilicon gate fabrication, gate etch stop on oxide, transistor gate patterning
Polysilicon etch is the process step that carves polycrystalline silicon into precise gate shapes, and for decades it was the single most critical etch step in the entire fab…
int8 fp8 int4 explained, llm quantization accuracy tradeoff, weight only quantization, activation quantization inference, quantization memory bandwidth ai accelerator
Quantization is the practice of storing and computing a model's weights and activations in fewer bits than the 16 or 32 bits they were trained in, trading a small amount…
Quantization-aware training is a technique that simulates the effects of reduced numerical precision during a neural network's training process itself, letting the model adapt…
quantum computing explained, what is a qubit, quantum computing meaning, quantum vs classical computing
Quantum computing is a fundamentally different approach to computation that uses quantum bits, or "qubits," instead of the ordinary bits every classical chip relies on — where…
ram explained, ram vs storage, random access memory, how does ram work
RAM, or random access memory, is the fast, temporary workspace a computer uses to hold whatever it's actively working on — open programs, loaded data, the current state of a game…
what is RAG AI
Retrieval-augmented generation, or RAG, is a technique that combines a language model with an external search step, retrieving relevant documents or facts from an outside…
sti semiconductor explained, transistor isolation fab process, sti etch fill cmp, front end of line isolation, sti trench oxide fill
Shallow trench isolation (STI) is the structure that keeps neighboring transistors electrically separate, and it's one of the earliest steps in the front-end-of-line process that…
draft model verification llm, speculative sampling explained, llm decode throughput optimization, draft and verify inference, accelerated token generation
Speculative decoding is a way to generate multiple tokens per expensive decode step instead of just one, by letting a small, cheap "draft" model guess ahead and having the large…
Strained silicon is a technique that deliberately stretches or compresses the silicon crystal lattice in a transistor's channel region, altering how easily electrons or holes move…
wafer probe test explained, dicing singulation semiconductor, chip packaging types, CoWoS advanced packaging, HBM TSV stacking, known good die
Once the last metal layer is done, a finished wafer is still just a fragile glass-thin disc holding hundreds of identical, untested chips. Everything up to this point…
wire bonding explained, wire bonding vs flip chip, chip wire bonding meaning, what does wire bonding mean
Wire bonding is the traditional method of connecting a silicon die to its package by attaching extremely thin metal wires between connection pads on the die's edges…
semiconductor yield explained, wafer yield meaning, why yield matters, chip manufacturing yield
Yield is the percentage of chips on a wafer that come out good enough to sell, and it is the single number that decides more about a chip's real-world cost than almost anything…
whisker formation tin lead prediction, whisker formation tin, formation tin lead, tin lead prediction, whisker formation, formation tin, tin lead, lead prediction
Whisker Formation Tin Lead Prediction with Machine Learning Introduction & Motivation Whisker Formation Tin Lead Prediction with Machine Learning addresses a central problem…
whisker growth risk prediction, whisker growth risk, growth risk prediction, whisker growth, growth risk, risk prediction
Whisker Growth Risk Prediction with Machine Learning Introduction & Motivation Whisker Growth Risk Prediction with Machine Learning addresses a central problem in tin whisker…
white light interferometry calibration drift prediction machine learning, white light, interferometry calibration drift, white light interferometry
White Light Interferometry Calibration Drift Prediction with Machine Learning Introduction & Motivation White Light Interferometry Calibration Drift Prediction with Machine…
Whitespace and Padding Normalization Introduction & Motivation Whitespace and Padding Normalization addresses a central problem in {subject} assurance and {target} management: how…
code ai
Whole Function Generation is the AI task of generating a complete, correct function implementation given only a natural language docstring and function signature — the primary…
300mm vs 200mm wafer, why are wafers 300mm, wafer size explained, why did wafers get bigger, 450mm wafer, dies per wafer
Modern fabs process silicon in the shape of 300-millimeter round wafers, not because that size is fundamentally special, but because it is the point where a bigger circle stopped…
why does a cpu need a heat sink, chip cooling explained, why do processors get hot, chip overheating
A chip needs cooling because every single transistor switching on and off wastes a small amount of energy as heat, and a modern processor switches billions of transistors billions…
Wide and Deep Learning for Memorization and Generalization Introduction & Motivation Wide and Deep Learning for Memorization and Generalization addresses a central problem…
Causal Process Modeling for Wide-Bandgap Power Devices Causal Process Modeling for Wide-Bandgap Power Devices Introduction Causal Process Modeling for Wide-Bandgap Power Devices…
Edge AI Deployment for Wide-Bandgap Power Devices Edge AI Deployment for Wide-Bandgap Power Devices Introduction Edge AI Deployment for Wide-Bandgap Power Devices is…
Predictive Maintenance for Wide-Bandgap Power Devices Predictive Maintenance for Wide-Bandgap Power Devices Introduction Predictive Maintenance for Wide-Bandgap Power Devices is…
Uncertainty Quantification for Wide-Bandgap Power Devices Uncertainty Quantification for Wide-Bandgap Power Devices Introduction Uncertainty Quantification for Wide-Bandgap Power…
Virtual Metrology Modeling for Wide-Bandgap Power Devices Virtual Metrology Modeling for Wide-Bandgap Power Devices Introduction Virtual Metrology Modeling for Wide-Bandgap Power…
model optimization
Width Multiplier is a scaling parameter that uniformly adjusts channel counts across a neural network - It offers a simple knob for trading off accuracy against compute…
graph neural networks
Wigner D-Matrix is rotation matrices for irreducible representation spaces used to transform equivariant feature channels - They provide the exact linear action of 3D rotations on…
environmental & sustainability
Wind Power PPA is procurement of wind-generated electricity through long-term power purchase agreements - It secures renewable supply and price visibility without owning…
micro-scale wind turbine, micro-scale wind, wind turbine
Micro-Scale Wind Turbine Introduction & Motivation Micro-Scale Wind Turbine addresses a central problem in energy-generation process control…
model optimization
Winning Tickets are the specific sparse sub-networks identified by the Lottery Ticket Hypothesis — sub-networks that, when trained from their original random initialization…
model training
Winning Tickets are the specific sparse sub-networks identified by the Lottery Ticket Hypothesis — sub-networks that, when trained from their original random initialization…
model optimization
Winograd Convolution is a fast convolution algorithm that reduces multiplications for small kernel sizes - It accelerates common convolutions in many vision models. What Is…
winograd convolution acceleration, winograd convolution, convolution acceleration
Winograd Convolution Acceleration Introduction & Motivation Winograd Convolution Acceleration addresses a central problem in hardware-accelerators process control…
wip queue time excursion prediction, wip queue time, queue time excursion, time excursion prediction, wip queue, queue time, time excursion, excursion prediction
WIP Queue Time Excursion Prediction with Machine Learning Introduction & Motivation WIP Queue Time Excursion Prediction with Machine Learning addresses a central problem…
wip queue time variability prediction, wip queue time, queue time variability, time variability prediction, wip queue, queue time, time variability, variability prediction
WIP Queue Time Variability Prediction with Machine Learning Introduction & Motivation WIP Queue Time Variability Prediction with Machine Learning addresses a central problem…
wire bond die attach process quality prediction machine learning, wire bond, die attach process, wire bond and die attach process quality prediction, wire bond die
Wire Bond and Die Attach Process Quality Prediction with Machine Learning Introduction & Motivation Wire Bond and Die Attach Process Quality Prediction with Machine Learning…
wire bond ball shear strength prediction machine learning, wire bond, ball shear strength, wire bond ball shear strength prediction, wire bond ball, bond ball shear, bond ball
Wire Bond Ball Shear Strength Prediction with Machine Learning Introduction & Motivation Wire Bond Ball Shear Strength Prediction with Machine Learning addresses a central problem…
wire bond capillary wear prediction, wire bond capillary, bond capillary wear, capillary wear prediction, wire bond, bond capillary, capillary wear, wear prediction
Wire Bond Capillary Wear Prediction with Machine Learning Introduction & Motivation Wire Bond Capillary Wear Prediction with Machine Learning addresses a central problem in wire…
failure analysis advanced
Wire bond FA is failure analysis focused on wire-bond integrity including lift, break, corrosion, and heel-crack mechanisms - Microscopy, pull tests, and electrical continuity…
wire bond loop height consistency prediction, wire bond loop, bond loop height, loop height consistency, height consistency prediction, wire bond, bond loop, loop height
Wire Bond Loop Height Consistency Prediction with Machine Learning Introduction & Motivation Wire Bond Loop Height Consistency Prediction with Machine Learning addresses a central…
wire bond loop height profile prediction machine learning, wire bond, loop height profile, wire bond loop height profile prediction, wire bond loop, bond loop height, bond loop
Wire Bond Loop Height Profile Prediction with Machine Learning Introduction & Motivation Wire Bond Loop Height Profile Prediction with Machine Learning addresses a central problem…