Results and Measurements¶

Measurements turn waveform data into design metrics. Specs turn those metrics into pass/fail decisions.

SimResult -> metric function -> Spec -> SpecTable -> corner/report view

Measurement Functions¶

Monata groups measurement helpers by domain:

Module	Use
`monata.measure.time_domain`	delay, rise/fall time, overshoot, settling
`monata.measure.freq_domain`	gain, bandwidth, phase margin, unity-gain frequency
`monata.measure.statistics`	histogram, yield, sensitivity, worst-case analysis
`monata.measure.spec`	pass/fail specification evaluation

A metric function should accept a SimResult and return a scalar.

def propagation_delay(result):
    time = result.sweep_var
    vin = result.waveforms["in"]
    vout = result.waveforms["out"]
    return delay(time, vin, vout, threshold=0.6)

Specs¶

Use Spec for one measurement and SpecTable for a group of checks.

from monata.measure.spec import SpecTable

specs = SpecTable()
specs.add("tpd", propagation_delay, max=80e-12, unit="s")
specs.add("voh", lambda r: max(r.waveforms["out"]), min=1.1, unit="V")

Evaluate specs after simulation:

table = specs.evaluate_all(corner_results)
worst_corner, worst_value = specs.worst_corner("tpd", corner_results)

Design Guidance¶

Keep metric functions small and explicit. They are the bridge between simulation and optimization, so they should not hide simulator setup or mutate the circuit.

Good metric functions:

read only from SimResult
return one number
handle missing or failed data deliberately
use clear units in the spec table

Avoid mixing backend-specific parsing into metric functions. If a backend needs special handling, normalize it when creating the SimResult.