Informatics Publishing Limited

Ranga Komanduri ¹, Prahalad Rao ², Prakash Vinod ³, Omer Beyca ², Sanjay Jagannath ⁴, Adam Fields ⁵, Zhenyu Kong ⁵, Satish Bukkapatnam ⁵

Affiliations
1 School of Mech., & Aerospace Engg, Oklahoma State University, Stillwater, Oklahoma, US
2 School of Industrial Engg., & Management, Oklahoma State University, Stillwater, Oklahoma, US
3 Central Manufacturing Technology Institute, Bangalore, IN
4 Dept. of Mech. Engg., Indian Institute of Technology, Chennai, Tamil Nadu, IN
5 School of Industrial Engg., & Management, Oklahoma State University, Stillwater, Oklahoma, IN

Abstract

This document presents the battery of tests conducted to ascertain the physical sources of dominant vibration sensor signal patterns observed in chemical mechanical planarization (CMP) process. A Buehler (model Automet® 250) bench top CMP machine is instrumented with miniature MEMS 3-axis accelerometer (Analog Devices ADXL 335) and audio sensors (Analog Devices ADMP 401). The CMP setup is used for finishing blanket copper workpieces to a surface finish of Ra ~ 5 nm. While the sensor signals are sensitive to variations in the CMP process, the extraneous noise prevents the direct use of raw signal patterns for early detection of defects. Consequently, instead of primarily monitoring the raw sensor signal patters, we isolate signal features which are indicative of process state from those which are mere artifacts, and thus potentially valuable for process monitoring.

The frequency spectrum of typical MEMS vibration sensor signals acquired during the CMP process contains three dominant components, namely:

1. Low frequency component in the 0.5 - 1 Hz region.

2. Broadband frequency regions centered around 25 Hz and 50 Hz.

3. Broadband frequency region around 120 Hz.

A total of nine tests are reported to ascertain the underlying physical cause of each of these components. Component 1 is shown to result from eccentricity errors in the polishing head (spindle); component 2 is most likely a conjoined effect due to sensor characteristics, electromagnetic interference from machine elements, and structural vibration; component 3 is observed to respond to changing downforce (polishing load) conditions, and process state, such as pad wear, and as such is useful for process monitoring applications. In addition, a relatively small (-110 dB) background (white) noise is evident throughout the frequency spectrum of CMP vibration signals; this is explained as originating from measurement errors and environmental factors.

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Satish T. S. Bukkapatnam ¹, Prahalada K. Rao ¹, Ranga Komanduri ¹

Affiliations
1 Oklahoma State University, Stillwater, OK 74075, US

Abstract

We present a sensor-based approach to estimate the quality of surfaces obtained during lapping-a common industrial polishing process-using an array of vibration sensors. We report the derivation and experimental validation of an analytical nonlinear stochastic model of lapping dynamics and the development of an online surface quality estimator for lapping based on combining sensor information with the model. The experiments were conducted on a Lapmaster bench-top lapping machine instrumented with three accelerometers. The results show that the model correctly captures the dynamics underlying the measured vibration signals including the shape of the complex frequency spectrum. The features were extracted from the acquired sensor data based on the model. The features were found to lead to linear dynamic maps that can estimate surface roughness to within 1% error under experimental conditions. Such predictability can help in reducing the operating cost of the lapping operation by minimizing scrap and rework.

Keywords

Lapping Process, Nonlinear Stochastic Dynamics, Surface Roughness, and Design of Experiments (DoE).

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