Data-driven surrogate model for etch rate profiles using sensor data from a reactive ion etcher

TL;DR

This paper presents a data-driven surrogate model that predicts etch rate profiles in semiconductor manufacturing using sensor data, reducing reliance on extensive metrology and enabling more reliable process control.

Contribution

The paper introduces a hybrid autoencoder-multiquadric interpolation model that predicts wafer etch rate profiles from sensor data, offering a novel approach to process monitoring.

Findings

Model accurately predicts etch rate profiles from sensor data.

Interpolation errors are analyzed for both interpolation and extrapolation.

The approach reduces the need for extensive metrology in manufacturing.

Abstract

Reliable predictions of the etch rate profile are desirable in semiconductor manufacturing to prevent etch rate target misses and yield rate excursions. Conventional methods for analyzing etch rate require extensive metrology, which adds considerable costs to manufacturing. We demonstrate a data-driven method to predict the etch rate profiles of a capacitively-coupled plasma RIE etcher from the tool's sensor data. The model employs a hybrid autoencoder-multiquadric interpolation-based approach, with the autoencoder being used to encode the features of the wafers' etch rate profiles into a latent space representation. The tool's sensor data is then used to construct interpolation maps for the latent space variables using multiquadric radial basis functions, which are then used to generate synthetic wafer etch rate profiles using the decoder. The accuracy of the model is determined using experimental data, and the errors are analyzed in interpolation and extrapolation.