Electrical Testing TRIZ Cases

Touch sensor electrodes use different frequencies in one burst to preserve border detection while cutting doze-mode power by 37%.

Hierarchical leaf and parent verification uses formal abstraction to reduce data transformation checks from millions of years to minutes.

Mechanical carrier positioning enables lower-cost fine-pitch DUT testing.

Latches and periodic drive signals detect floating pins while limiting quiescent current.

Estimate battery SOC and SOH from voltage, avoiding inaccurate current measurements.

Cell-based electrostatic analysis adjusts charged-particle density and advection to predict discharge between electrodes.

A sample-and-hold circuit captures battery voltage before boot-up loading, improving open-circuit voltage and SoC estimation.

Sequential SOH then SOC estimation uses NSSR-LSTM to handle cell variation and nonlinear aging, with errors below 2.5%.

Differential-capacity peaks reveal subtle impedance changes in electrochemical cells.

A hybrid ageing and data-based model uses operating history and usage patterns to improve battery SOH forecasts and quantify uncertainty.

This circuit observes uncovered functional logic through existing flip-flops, avoiding added observability flops, delay, and cost.

Gaussian process regression uses battery measurements to estimate SoC while quantifying uncertainty for more reliable charge management.

Separate capacitor-resistor paths isolate outputs and indicators while preserving phase alignment in high-voltage networks.

Peer-cell behavior monitoring flags voltage and current outliers early, without extensive historical data or complex models.

Vectorial magnetic-field sensing at a conductor node enables rapid, precise current measurement across multiple conductors with one device.

Multiplexers bypass selected cell stages, restoring diagnostic resolution for more complete integrated-circuit testing.

Voltage and current trends across cell groups reveal outliers and trigger early alerts without complex battery models.

This case evolves and evaluates symbolic equations to identify interpretable causes of energy storage degradation.

This case splits battery health into charge and discharge phases to calculate overall SOH during normal operation.

Voltage, current, and temperature density features support machine-learning estimates for timely battery replacement decisions.

Partial discharge data is preprocessed for AI training and inference, enabling faster, accurate battery SoH evaluation.

Test data runs across compute units, and matching results exposes functional safety abnormalities for timely circuit protection.

An adjustable jig presses protective sheets onto auto visual inspection stages, reducing maintenance time and standardizing replacement.

This case addresses blocked mmW transmission by separating gripping and signal paths in a compact, ESD-controlled test kit.

This battery quality case adapts limit curves to voltage changes, reducing misjudgments and preserving precision during stable charging.

A dense film in the Pd-Ag-Ni-Cu probe suppresses solder diffusion and wear, stabilizing contact resistance during inspection.

Orthogonal grooves and nested pillars provide two-dimensional plate adjustment, reducing fixture bulk, tuning time, and alignment risk.

Robotic sensors and AI scan used batteries to assess SOH and RUL, guiding reuse, repurposing, or recycling decisions.

A dataflow-aware compiler staggers compute-unit activation to limit current ramp changes and stabilize integrated-circuit supply voltage.

This case uses charging and discharging voltage indicators to correct module capacity and improve consistency across a battery system.

The input detector uses pressing-force order and a special mode to validate only intentional contacts.