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Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants

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Dynamic neural control for a plasma etch process.

J P Card¹, D L Sniderman, C Klimasauskas

¹Digital Equipment Corp., Hudson, MA.

IEEE Transactions on Neural Networks

|January 1, 1997

Summary

This study introduces a neural network and optimization method to restore plasma etcher performance after machine drift. The approach effectively suggests setpoints, reducing downtime and costs.

Area of Science:

Semiconductor Manufacturing
Artificial Intelligence in Process Control
Plasma Etching Technology

Background:

Long-term machine drift in dielectric etchers like the LAM 4520 impacts etch rates, uniformity, and selectivity.
Traditional methods for recovery can be slow and costly, leading to increased equipment downtime.
Predictive modeling is needed to account for process variations and maintenance events.

Purpose of the Study:

To develop and validate a predictive model for plasma etch processes using a cascade neural network.
To implement policy-iteration optimization routines for suggesting process setpoints to recover from machine drift.
To reduce equipment downtime and the cost of ownership for semiconductor manufacturing tools.

Main Methods:

A cascade neural network model was developed using 15 months of LAM 4520 dielectric etcher data.

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The model integrates traditional plasma etch variables with time-dependent effects of maintenance events.

Two control algorithms utilized the neural model in a predictive configuration for out-of-control recovery.

Main Results:

The neural network model accurately fit validation data and captured process nonuniformity.
The optimization routines successfully suggested low-cost recovery solutions for 11 out-of-control situations.
The predictive control approach demonstrated effectiveness in bringing the system back into control.

Conclusions:

The combined cascade neural network and policy-iteration approach provides an effective method for recovering plasma etchers from machine drift.
This methodology can significantly reduce equipment downtime and extend the lifetime of consumable parts.
The developed system offers a pathway to lower the overall cost of ownership for semiconductor manufacturing tools.