Line Edge Roughness Estimator

Estimate 3-sigma line edge roughness as dose sensitivity in nanometers per percent times dose variation percent, using a documented heuristic.

Description

Estimate 3-sigma line edge roughness as dose sensitivity in nanometers per percent times dose variation percent, using a documented heuristic.

Line Edge Roughness Estimator is a focused tool for the following task. Estimate 3-sigma line edge roughness as dose sensitivity in nanometers per percent times dose variation percent, using a documented heuristic. It reports 3-sigma LER from the values you provide rather than inventing measurements, coefficients, or professional judgment that are not part of the input.

When to use Line Edge Roughness Estimator

Use this semiconductor calculation for first-order device, material, fabrication, interconnect, packaging, or reliability estimates when every coefficient and unit convention is known.

Dose variation (%)
Required number in %. Exposure dose variation driving roughness.
LER sensitivity (nm/%)
Required number in nm/%. Line edge roughness sensitivity to dose, documented heuristic factor.

The cited overview of Photolithography supplies background for the terminology and domain context used by this tool.1

How Line Edge Roughness Estimator works

Estimate 3-sigma line edge roughness as dose sensitivity in nanometers per percent times dose variation percent, using a documented heuristic. Inputs are interpreted exactly in the displayed units and the calculation returns the following fields without presentation rounding.

3-sigma LER (nm)
Returned number in nm. Estimated three-sigma line edge roughness.

Limitations and assumptions

  • Material composition, geometry, process history, temperature, electric field, bias, interfaces, parasitics, and fitted parameter ranges can invalidate a compact semiconductor model.
  • Dose variation must be at least 0.
  • LER sensitivity must be at least 0.
  • Use finite inputs in the displayed units, preserve source measurements and assumptions, and independently verify consequential decisions.

Alternative or Complementary approaches

Compare the estimate with measured process data, current device documentation, and a higher-fidelity circuit, field, thermal, quantum, or TCAD model when the decision requires it.

References

  1. Photolithography — Wikipedia contributors

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