Numetiva

Engineering intelligence. On your PC.

Complex data.
Clearer decisions.

Statistics, wafer maps, local AI and Python in one connected engineering workbench. See the pattern. Follow the question. Keep the insight.

Discover Numetiva Arrange a walkthrough
Built around the way engineers think.
Variation, made visible.
Detail of a Python-generated wafer map showing oxide-thickness variation across die coordinates.Explore wafer maps
Statistical analysisSemiconductor insightsVisual workflowsLocal AI & Python

One connected engineering environment

138Analysis options31Graph optionsLocalPython & AI on your PC

A closer look

See more.
Understand more.

From distributions to die-level patterns. Move between the views that bring your engineering question into focus.

Inside Numetiva

Every view.
A new way in.

Explore the workbench data: process capability, predictive response and die-level patterns. Open a chart to see the detail.

Neural network profilerNUMETIVA
Numetiva saved-scene vector view: neural network prediction profiler showing the response Y against X, Z and Line, with the selected predictor settings marked.
Explore predictive profilers ↗See multi-wafer views ↗

Your work, connected

From the first column
to the final decision.

Explore a measurement. Build an analysis. Turn the useful steps into a workflow you can return to.

Explore visual workflows
  1. 01

    Start with a better view.

    Explore distributions, compare groups and connect a visible pattern with its underlying measurements.

  2. 02

    Keep the analysis together.

    Move between charts, fitted models, profilers and their related text in the project output navigator.

  3. 03

    Make it part of the workflow.

    Save the sequence, return to a Python script and take a clear chart or report into your next review.

AI, within the workbench

A little help.
A lot more possibility.

Ask in plain language. Inspect the Python draft. Run it with your worksheet. Numetiva AI stays connected to the work you are doing.

N / 02

Your question is
a useful starting point.

Plot relationships. Explore a model. Bring an error back into the conversation and work through the next step.

Interactive product tourOn your PC
YOUR QUESTION

“Plot tool wear against vibration, and add a quadratic fit.”

Numetiva AI

Turn the question into a script you can inspect.

Python workbenchDraft
import numpy as npimport matplotlib.pyplot as pltx = df["Tool_Wear_um"]y = df["Vibration_RMS_g"]ax.scatter(x, y)fit = np.polyfit(x, y, 2)

Local by design

Your data.
Your environment.
Your workbench.

Run engineering analysis close to the data, with local statistical tools, an offline AI option and a Python workbench on your Windows PC.

Explore local operation

Bring your engineering question.

Explore how Numetiva fits your data, workflow and review requirements.

Talk to Numetiva
Numetiva / vector chart
100%Open SVG ↗

Zoom for individual dies and labels. Drag or scroll to pan. + / - to zoom; 0 to fit.

Numetiva / interactive product tour

From a question to a checked output

Your question.
A reviewable script.

Use the assistant to draft Python, inspect the code in the workbench, then run it against your worksheet. Bring the latest error into Review / fix when the script needs attention.

  1. 01
    Ask with context.

    Name the response, predictors and the chart you need.

  2. 02
    Inspect the draft.

    Check the column names, transformations and method.

  3. 03
    Run and review.

    Use the returned output and errors to guide the next step.

Explore AI & Python
Python / exampleWorksheet context
import numpy as np
import matplotlib.pyplot as plt

x = df["Tool_Wear_um"].to_numpy()
y = df["Vibration_RMS_g"].to_numpy()
valid = np.isfinite(x) & np.isfinite(y)
x, y = x[valid], y[valid]

fig, ax = plt.subplots()
ax.scatter(x, y, s=18, alpha=0.7)
coefficients = np.polyfit(x, y, 2)
grid = np.linspace(x.min(), x.max(), 200)
ax.plot(grid, np.polyval(coefficients, grid))
ax.set(xlabel="Tool wear / µm",
       ylabel="Vibration / g")
plt.show()
About this product tour