2,942 technical terms and definitions
sraf placement optimization, scattering bar insertion, assist feature opc model, subresolution scattering bars
Sub-Resolution Assist Features (SRAFs), also designated as scattering bars or assist features, are narrow, non-printing reticle structures positioned strategically adjacent…
training techniques
Subsampling is training strategy that processes randomly selected subsets of data per optimization step - It is a core method in modern semiconductor AI serving and trustworthy-ML…
subsequence similarity search, subsequence similarity, similarity search
Subsequence Similarity Search Introduction & Motivation Subsequence Similarity Search addresses a central problem in timeseries process control and subsequence-matching-query…
subset sum approximation, subset sum, sum approximation
Subset Sum Approximation Introduction & Motivation Subset Sum Approximation addresses a central problem in algorithms process control and approximation-ratio-guarantee prediction…
subset sum partition problem prediction, subset sum partition, sum partition problem, partition problem prediction, subset sum, sum partition, partition problem, problem prediction
Subset Sum Partition Problem Prediction with Machine Learning Introduction & Motivation Subset Sum Partition Problem Prediction with Machine Learning addresses a central problem…
domain adaptation
Subspace Alignment is a domain adaptation method that aligns the source and target domains by finding and aligning their respective subspaces—learned through PCA or other…
Subspace Learning for Anomaly Detection Introduction & Motivation Subspace Learning for Anomaly Detection addresses a central problem in anomaly detection and {target} scoring…
Subspace Outlier Detection SOD in Arbitrary Angles Introduction & Motivation Subspace Outlier Detection SOD in Arbitrary Angles addresses a central problem in anomaly detection…
Subspace Transfer Learning Introduction & Motivation Subspace Transfer Learning addresses a central problem in transfer learning and {target} adaptation: how to Apply {subject}…
defects
Substitutional Impurity is the foreign atom occupying a regular lattice site by replacing a host atom — it is the desired configuration for all electrically active dopants…
substrate blind laser drill defect prediction machine learning, substrate blind, laser drill defect, substrate blind via laser drill defect prediction, blind via laser
Substrate Blind Via Laser Drill Defect Prediction with Machine Learning Introduction & Motivation Substrate Blind Via Laser Drill Defect Prediction with Machine Learning addresses…
substrate core via registration prediction, substrate core via, core via registration, via registration prediction, substrate core, core via, via registration
Substrate Core Via Registration Prediction with Machine Learning Introduction & Motivation Substrate Core Via Registration Prediction with Machine Learning addresses a central…
substrate noise, guard ring isolation, mixed signal substrate, noise coupling analog digital
CMOS latch-up constitutes the destructive, self-sustaining low-impedance state triggered by the regenerative turn-on of parasitic bipolar junction transistors inherent to bulk…
substrate coupling model verification prediction, substrate coupling model, coupling model verification, model verification prediction, substrate coupling
Substrate Coupling Model Verification Prediction with Machine Learning Introduction & Motivation Substrate Coupling Model Verification Prediction with Machine Learning addresses…
substrate coupling noise prediction, substrate coupling noise, coupling noise prediction, substrate coupling, coupling noise, noise prediction
Substrate Coupling Noise Prediction with Machine Learning Introduction & Motivation Substrate Coupling Noise Prediction with Machine Learning addresses a central problem…
substrate current impact ionization modeling prediction, substrate current impact, current impact ionization, impact ionization modeling, ionization modeling prediction
Substrate Current Impact Ionization Modeling Prediction with Machine Learning Introduction & Motivation Substrate Current Impact Ionization Modeling Prediction with Machine…
substrate integrated waveguide propagation loss, substrate integrated waveguide, integrated waveguide propagation, waveguide propagation loss, substrate integrated
Substrate Integrated Waveguide Propagation Loss with Machine Learning Introduction & Motivation Substrate Integrated Waveguide Propagation Loss with Machine Learning addresses…
coupling, mixed signal, noise injection, substrate tap, deep nwell, guard ring
CMOS latch-up constitutes the destructive, self-sustaining low-impedance state triggered by the regenerative turn-on of parasitic bipolar junction transistors inherent to bulk…
design, mixed-signal design, triple-well isolation, guard ring, analog noise sensitivity, PLL jitter, ADC noise, digital switching noise, SoC floor planning
Substrate noise coupling is the unwanted transfer of electrical noise through the shared silicon substrate underneath every transistor on a chip — switching digital circuits…
substrate panel incoming warpage prediction machine learning, substrate panel, incoming warpage prediction, substrate panel incoming warpage prediction, panel incoming
Substrate Panel Incoming Warpage Prediction with Machine Learning Introduction & Motivation Substrate Panel Incoming Warpage Prediction with Machine Learning addresses a central…
substrate panel singulation defect prediction machine learning, substrate panel, singulation defect prediction, substrate panel singulation defect prediction
Substrate Panel Singulation Defect Prediction with Machine Learning Introduction & Motivation Substrate Panel Singulation Defect Prediction with Machine Learning addresses…
substrate resistivity uniformity prediction, substrate resistivity uniformity, resistivity uniformity prediction, substrate resistivity, resistivity uniformity
Substrate Resistivity Uniformity Prediction with Machine Learning Introduction & Motivation Substrate Resistivity Uniformity Prediction with Machine Learning addresses a central…
substrate solder mask peel prediction machine learning, substrate solder, mask peel prediction, substrate solder mask peel prediction, substrate solder mask, solder mask peel
Substrate Solder Mask Peel Prediction with Machine Learning Introduction & Motivation Substrate Solder Mask Peel Prediction with Machine Learning addresses a central problem…
substrate solder mask registration prediction, substrate solder mask, solder mask registration, mask registration prediction, substrate solder, solder mask
Substrate Solder Mask Registration Prediction with Machine Learning Introduction & Motivation Substrate Solder Mask Registration Prediction with Machine Learning addresses…
substrate supplier defect correlation prediction, substrate supplier defect, supplier defect correlation, defect correlation prediction, substrate supplier
Substrate Supplier Defect Correlation Prediction with Machine Learning Introduction & Motivation Substrate Supplier Defect Correlation Prediction with Machine Learning addresses…
substrate trace impedance drift prediction machine learning, substrate trace, impedance drift prediction, substrate trace impedance drift prediction, trace impedance drift
Substrate Trace Impedance Drift Prediction with Machine Learning Introduction & Motivation Substrate Trace Impedance Drift Prediction with Machine Learning addresses a central…
design
Subthreshold computing (also called sub-Vth or near-threshold computing) operates transistors at supply voltages below the threshold voltage ( ) — exploiting the weak inversion…
subthreshold leakage current scaling and power dissipation, subthreshold leakage current, leakage current scaling, current scaling power, scaling power dissipation, subthreshold leakage
Subthreshold Leakage Current Scaling and Power Dissipation Introduction & Motivation Subthreshold Leakage Current Scaling and Power Dissipation addresses a central problem…
subthreshold swing degradation prediction, subthreshold swing degradation, swing degradation prediction, subthreshold swing, swing degradation, degradation prediction
Subthreshold Swing Degradation Prediction with Machine Learning Introduction & Motivation Subthreshold Swing Degradation Prediction with Machine Learning addresses a central…
subthreshold swing temperature dependence prediction, subthreshold swing temperature, swing temperature dependence, temperature dependence prediction, subthreshold swing
Subthreshold Swing Temperature Dependence Prediction with Machine Learning Introduction & Motivation Subthreshold Swing Temperature Dependence Prediction with Machine Learning…
process integration
Subtractive Etch BEOL is a BEOL process flow where blanket metal is deposited and then etched to define interconnect lines - It provides direct pattern control for metals less…
subtractive patterning metal, semi damascene, metal etch interconnect, subtractive ru
Copper dual damascene interconnect architectures, electrochemical superfilling, and barrier-seed metallization constitute the back-end-of-line (BEOL) wiring systems that route…
first silicon success, first silicon, success, working chips, yield rate
Chip Foundry Services achieves 95%+ first-silicon success rate — meaning 95% of our designs work correctly on first fabrication compared to 60-70% industry average, with our…
reliability
Success run theorem is the statistical rule that links a run of zero failures to demonstrated reliability at a chosen confidence level - it provides quick planning equations…
reliability
Success testing is reliability demonstration testing where passing is based on observing zero or very few failures in a defined test - Given sample size and test duration, success…
successive approximation adc settling error prediction, successive approximation adc, approximation adc settling, adc settling error, settling error prediction
Successive Approximation ADC Settling Error Prediction with Machine Learning Introduction & Motivation Successive Approximation ADC Settling Error Prediction with Machine Learning…
successive halving early elimination and resource efficiency, successive halving early, halving early elimination, early elimination resource, elimination resource efficiency
Successive Halving Early Elimination and Resource Efficiency Introduction & Motivation Successive Halving Early Elimination and Resource Efficiency addresses a central problem…
Successive Halving Algorithm Introduction & Motivation Successive Halving Algorithm addresses a central problem in optimization algorithm tuning and improvement: how to Optimize…
quality & reliability
Successive Inspection is an immediate handoff check where the next operation verifies prior-step quality before continuing - It is a core method in modern semiconductor quality…
reinforcement learning advanced
Successor features is representations that decompose value into expected feature occupancy and task-specific reward weights - Feature dynamics learned once can transfer quickly…
reinforcement learning advanced
Successor Representation is state representation of expected discounted future occupancy used for transferable value prediction. - It separates environment dynamics from reward…
Successor Representation and State Occupancy Introduction & Motivation Successor Representation and State Occupancy addresses a central problem in reinforcement policy learning…
sufficient dimension reduction (sdr) prediction, sufficient dimension reduction, dimension reduction (sdr), reduction (sdr) prediction, sufficient dimension, reduction (sdr)
Sufficient Dimension Reduction (SDR) Prediction with Machine Learning Introduction & Motivation Sufficient Dimension Reduction (SDR) Prediction with Machine Learning addresses…
suffix array construction, suffix array, array construction
Suffix Array Construction Introduction & Motivation Suffix Array Construction addresses a central problem in data-structures process control and array-construction-linear-time…
suffix tree construction, suffix tree, tree construction
Suffix Tree Construction Introduction & Motivation Suffix Tree Construction addresses a central problem in data-structures process control and construction-time-linear prediction…
quality & reliability
Suggestion System is a structured channel for collecting, evaluating, and acting on frontline improvement ideas - It is a core method in modern semiconductor operational…
compress, distill
Text Summarization with LLMs Summarization Approaches Extractive Summarization Select and combine important sentences from source text. Preserves original wording May miss key…
prompting
Summarization for context is the practice of replacing long dialogue or document history with condensed summaries to preserve key information within token limits - it is a core…
Abstractive Text Summarization Introduction & Motivation Abstractive Text Summarization addresses a central problem in generative generation and summary-fidelity-compression…
condense, tldr
Summarization condenses long documents into shorter versions that capture the key points, main arguments, and essential information while omitting redundant or less important…