Data Driven Project.

The Project Objective

Objective type:

The Metric of Interest (MOI):

MOI objective:

Second Metric of Interest (MOI2):

MOI2 objective:

Factor type:

KPI: MOI target (optional):

KPI: Target for MOI spread within a sample (3-sigma) (optional):

Select the project stage below

Stage 1 | Exploratory

Stay mission-driven with exploration

There are many possible routes to improve a project's starting point for a given objective - new materials, new device designs, new fabrication methods, new tool designs, and more. The key is choosing the right directions to explore first.

Exploration Prioritization Guidance

1) Describe the potential mission impact clearly.
Explain what new capability or performance window the direction could open relative to the project objective, and use the Pathfinder framework to assess mission relevance.

2) Prioritize by quantitative value.
Evaluate timeline and expected impact in MOIs (Quality, Speed, Scale, OpEx, CapEx) and expected impact on device functionality.

Open Pathfinder Framework: https://the-pathfinder-framework.qfcph.com/

Stage 2 | Reproducibility

SAMPLE TO SAMPLE VARIATION ANALYSIS
Process control verification to confirm stable output and repeatability before screening.

Reproducibility Setup

Target for Subject ID-to-Subject ID variability (%). Note: Aim for lower variability than expected effect sizes in Stage 3. Target for Subject ID-to-Subject ID variability for MOI2 (%).

Percent variability lines are shown in the plot together with sigma lines.

Number of samples with identical sets of PCPs (n):

Number of PMPs per subject ID:

MOI (mean) values and PMPs from all n Subject IDs

MOI2 values from all n Subject IDs

Enter or load values, then click 'Plot Reproducibility'.

MOI vs PMP linear correlations

Correlation coefficient (r) describes the strength and direction of the linear relationship between MOI and PMP, from -1 to +1.
P-value (p) measures evidence against no linear correlation; a smaller p-value indicates stronger evidence of a relationship.
Add PMP values and click 'Plot results' to calculate correlations.

Variability troubleshooting

Understand the Sources of Variability

Run-to-run variability can arise from several sources. Here are some suggestions to consider:
- Equipment drift (e.g., temperature fluctuations, calibration shifts). Check PMPs.
- Material batch differences? (e.g., substrate properties, chemical purity)
- Operator differences (e.g., manual handling, setup inconsistencies). Automize and standardize.
- Environmental factors (e.g., humidity, vibrations, electromagnetic interference). Log these as PMPs.
- Measurement noise (e.g., instrument precision, readout errors)

Experiment Setup

Number of factors (PCPs):

Screening DOE type:

Set PCP count and names before suggesting DOE.

Process Control Parameters (PCPs)

Click 'Set PCP Count' to generate PCP name, low, high, and units fields.

Ready

After experiments

Generate DOE first, then enter MOI Mean (and optional Spread) for each Subject ID.

Effect ranking output will appear here after entering run MOI values.

Effect ranking output for MOI2 will appear here after entering run MOI2 values.

Stage 4 | Optimization

After identifying the most critical PCPs with the largest effect on the MOI in Stage 3, optimize the MOI using Response Surface Methodology (RSM) with a quadratic model. Use Central Composite Design (CCD) to capture the true optimum efficiently.

Use Stage 3 effect ranking to choose key PCPs, then set low/center/high levels for CCD planning.

Typical CCD run count: 2^k + 2*k + center points, where k is the number of key PCPs. Enter the shortlisted key PCPs below and select the best optimization region.

Optimization Setup (CCD)

Optimization method:

Number of key PCPs (k):

Center points:

Key PCPs for CCD

First execute the Subject IDs for the basic factorial and center points DOE - get the DOE optimization matrix here.

After executing the basic optimization DOE add adaptive axial points here for finetuning optimum.

After entering key PCPs, click 'Suggest CCD Runs' to generate the RSM/CCD run table.

After experiments

Generate suggested CCD runs first. Then enter MOI mean for each run.

MOI vs MOI2 weight (%):

Optimization Visualization