Sintering is a critical interconnect method in power electronics, enabling reliable die-attach and substrate bonding under high current and thermal stress. Robust joints require precise control of pressure, temperature, atmosphere, and dwell time, as variations can cause voids, delamination, or early failure.

Accelerates convergence to optimal settings by identifying which parameters truly matter under realistic boundary conditions

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Reduces risk of overfitting process windows by linking mechanical and thermal test outcomes with rich experimental metadata

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Improves knowledge retention by standardizing the way process trials are documented and shared across teams

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Enables scale-up by offering detailed comparison of lab, pilot, and production-scale sintering condition

• Engineers work in the yieldmanager:
• Define parameter sets (force, ramp rate, gas type)
• Capture time-series data from press and sensors
• Log metadata: operator, batch, equipment, recipe version
• Link test results (e.g. shear strength, voids) to conditions
• Analyze trends with built-in correlation and regression
• Optionally integrate FEM simulations
• Store all runs in a searchable, versioned database

A stable and reproducible sintering process across materials, operators, and equipment generations

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Quantified parameter influence using real production data, enabling confident release of new material combinations

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Future-proof process memory, including raw sensor data and interpreted insights, fully versioned and linked to product releases

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Foundation for machine learning applications to proactively detect drift or predict batch outcomes

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