104771
channel strain engineering drift machine learning, modeling drift in channel strain during high-temperature processing, modeling drift channel, drift channel strain, channel strain during, strain during high-temperature, modeling drift
14,989 technical terms and definitions
channel strain engineering drift machine learning, modeling drift in channel strain during high-temperature processing, modeling drift channel, drift channel strain, channel strain during, strain during high-temperature, modeling drift
monolithic 3d layer alignment machine learning, predicting monolithic 3d layer alignment quality and defect rates, predicting monolithic 3d, monolithic 3d layer, 3d layer alignment, layer alignment quality, alignment quality defect
sequential device thermal impact machine learning, modeling cumulative thermal budget impact on device performance in sequential processing, modeling cumulative thermal, cumulative thermal budget, thermal budget impact, budget impact device
euv resist line roughness prediction machine learning, predicting line-width roughness (lwr) from euv resist formulation parameters, predicting line-width roughness, line-width roughness (lwr), roughness (lwr) from, (lwr) from euv, from euv
mask defect printability prediction machine learning, determining which mask defects will degrade print fidelity and impact yield, determining which mask, which mask defects, mask defects will, defects will degrade, will degrade print
euv pellicle contamination monitoring machine learning, predicting pellicle contamination accumulation rate for maintenance scheduling, predicting pellicle contamination, pellicle contamination accumulation, contamination accumulation rate
high na imaging fidelity prediction machine learning, predicting imaging fidelity degradation at high numerical apertures, predicting imaging fidelity, imaging fidelity degradation, fidelity degradation high, degradation high numerical
optical proximity correction accuracy machine learning, improving opc model accuracy for sub-5nm lithography, improving opc model, opc model accuracy, model accuracy sub-5nm, accuracy sub-5nm lithography, improving opc, opc model
nand string resistance prediction machine learning, predicting nand string resistance from cell design and processing, predicting nand string, nand string resistance, string resistance from, resistance from cell, from cell design, from cell
dram refresh rate optimization machine learning, optimizing refresh rates in dram to balance power and data retention, optimizing refresh rates, refresh rates dram, rates dram to, dram to balance, to balance power, balance power data
floating gate leakage drift machine learning, modeling leakage drift in floating-gate devices during operation, modeling leakage drift, leakage drift floating-gate, drift floating-gate devices, floating-gate devices during, modeling leakage
memory cell variability modeling machine learning, characterizing and predicting cell-to-cell variability in memory arrays, characterizing predicting cell-to-cell, predicting cell-to-cell variability, cell-to-cell variability memory
rf pa output matching drift machine learning, predicting rf pa output matching drift over temperature and frequency, predicting rf pa, rf pa output, pa output matching, output matching drift, matching drift over, drift over temperature
lna noise figure drift prediction machine learning, modeling noise figure degradation in lnas due to process and environmental drift, modeling noise figure, noise figure degradation, figure degradation lnas, degradation lnas due, lnas due
voltage reference temperature drift machine learning, predicting bandgap reference voltage drift over temperature range, predicting bandgap reference, bandgap reference voltage, reference voltage drift, voltage drift over, bandgap reference
pll frequency accuracy prediction machine learning, predicting pll frequency accuracy from design parameters and pvt variations, predicting pll frequency, pll frequency accuracy, frequency accuracy from, accuracy from design, predicting pll
pressure sensor nonlinearity drift machine learning, modeling pressure sensor nonlinearity drift with temperature and age, modeling pressure sensor, pressure sensor nonlinearity, sensor nonlinearity drift, nonlinearity drift temperature
temperature sensor accuracy calibration machine learning, optimizing temperature sensor calibration across wide operational ranges, optimizing temperature sensor, temperature sensor calibration, sensor calibration across, temperature sensor
accelerometer cross axis sensitivity machine learning, predicting and compensating for cross-axis sensitivity in accelerometers, predicting compensating cross-axis, compensating cross-axis sensitivity, cross-axis sensitivity accelerometers
buck converter efficiency prediction machine learning, predicting buck converter efficiency across load and voltage ranges, predicting buck converter, buck converter efficiency, converter efficiency across, efficiency across load
linear regulator psrr prediction machine learning, modeling power supply rejection ratio (psrr) across frequency, modeling power supply, power supply rejection, supply rejection ratio, rejection ratio (psrr), ratio (psrr) across
serdes equalization coefficient drift machine learning, predicting optimal equalization coefficients for serdes links under signal degradation, predicting optimal equalization, optimal equalization coefficients, coefficients serdes links
cdr jitter transfer function prediction machine learning, modeling clock and data recovery (cdr) jitter transfer functions for link design, modeling clock data, clock data recovery, data recovery (cdr), recovery (cdr) jitter, modeling clock
io impedance matching accuracy machine learning, predicting i/o impedance matching accuracy in high-speed digital interfaces, predicting i/o impedance, i/o impedance matching, impedance matching accuracy, matching accuracy high-speed
superconducting qubit coherence loss machine learning, modeling coherence time loss (t1 t2) in superconducting qubits, modeling coherence time, coherence time loss, time loss (t1, loss (t1 t2), (t1 t2) superconducting, modeling coherence
quantum dot charge stability machine learning, predicting charge stability in quantum dot qubits for improved readout fidelity, predicting charge stability, charge stability quantum, stability quantum dot, quantum dot qubits, quantum dot
tensor core yield prediction machine learning, predicting tensor core yield and performance uniformity on ai accelerators, predicting tensor core, tensor core yield, core yield performance, yield performance uniformity, predicting tensor
metal via resistance aging prediction machine learning, predicting metal via resistance aging over device lifetime, predicting metal via, metal via resistance, via resistance aging, resistance aging over, aging over device, predicting metal
electromigration current crowding hotspot machine learning, detecting and locating current crowding hotspots for em risk, detecting locating current, locating current crowding, current crowding hotspots, crowding hotspots em, hotspots em
defect classification ai model training machine learning, training ai models for automated defect classification from inspection images, training ai models, ai models automated, models automated defect, automated defect classification
wafer edge defect rate prediction machine learning, predicting wafer edge defect rate and peripheral loss, predicting wafer edge, wafer edge defect, edge defect rate, defect rate peripheral, rate peripheral loss, predicting wafer
silicon interposer thermal resistance machine learning, predicting thermal resistance in silicon interposers, predicting thermal resistance, thermal resistance silicon, resistance silicon interposers, predicting thermal, thermal resistance
stress induced leakage current machine learning, modeling stress-induced leakage current (silc) in gate dielectrics, modeling stress-induced leakage, stress-induced leakage current, leakage current (silc), current (silc) gate, (silc) gate
gate oxide breakdown time modeling machine learning, predicting gate oxide time-dependent breakdown (tddb) time-to-failure, predicting gate oxide, gate oxide time-dependent, oxide time-dependent breakdown, time-dependent breakdown (tddb)
dielectric breakdown field modeling machine learning, modeling dielectric breakdown electric field from material properties, modeling dielectric breakdown, dielectric breakdown electric, breakdown electric field, electric field from
transconductance temperature coefficient machine learning, predicting transconductance temperature coefficient for analog circuit design, predicting transconductance temperature, transconductance temperature coefficient, coefficient analog
output impedance frequency dependent prediction machine learning, modeling frequency-dependent output impedance in analog circuits, modeling frequency-dependent output, frequency-dependent output impedance, output impedance analog
bulk biasing efficiency optimization machine learning, optimizing bulk biasing efficiency for power reduction, optimizing bulk biasing, bulk biasing efficiency, biasing efficiency power, efficiency power reduction, optimizing bulk
backgate coupling capacitance prediction machine learning, predicting backgate coupling capacitance in multi-gate devices, predicting backgate coupling, backgate coupling capacitance, coupling capacitance multi-gate, predicting backgate
reticle design manufacturability scoring machine learning, scoring reticle designs for manufacturability using machine learning, scoring reticle designs, reticle designs manufacturability, designs manufacturability using, scoring reticle
mask write time optimization prediction machine learning, predicting and optimizing e-beam mask write time, predicting optimizing e-beam, optimizing e-beam mask, e-beam mask write, mask write time, predicting optimizing, optimizing e-beam
litho adjacency effect correction ml machine learning, correcting lithography adjacency effects using machine learning, correcting lithography adjacency, lithography adjacency effects, adjacency effects using, effects using machine
etch rate resist proximity variation machine learning, predicting etch rate variation due to resist proximity and pattern, predicting etch rate, etch rate variation, rate variation due, variation due to, due to resist, to resist proximity
wirebond pull test prediction ml machine learning, predicting wire bond pull test strength from bonding parameters, predicting wire bond, wire bond pull, bond pull test, pull test strength, test strength from, strength from bonding
die attach curing time optimization machine learning, optimizing die attach adhesive curing time and temperature profile, optimizing die attach, die attach adhesive, attach adhesive curing, adhesive curing time, curing time temperature
humidity sensitivity calibration drift machine learning, modeling humidity sensitivity and calibration drift, modeling humidity sensitivity, humidity sensitivity calibration, sensitivity calibration drift, modeling humidity
altitude pressure compensation model machine learning, developing altitude and pressure compensation models, developing altitude pressure, altitude pressure compensation, pressure compensation models, developing altitude, altitude pressure
wafer lot traceability defect correlation machine learning, correlating wafer lot properties with downstream defect rates, correlating wafer lot, wafer lot properties, lot properties downstream, properties downstream defect, wafer lot
neuromorphic crossbar defect machine learning, detecting and localizing defects in memristor crossbar arrays used in neuromorphic chips, detecting localizing defects, localizing defects memristor, defects memristor crossbar, crossbar arrays
electromigration lifetime prediction machine learning, predicting mean-time-to-failure (mttf) from electromigration in metal interconnects, predicting mean-time-to-failure (mttf), mean-time-to-failure (mttf) from, (mttf) from