Proceedings Article | 24 May 2004
Proc. SPIE. 5375, Metrology, Inspection, and Process Control for Microlithography XVIII
KEYWORDS: Scatterometry, Semiconducting wafers, Scatter measurement, Scanning electron microscopy, Critical dimension metrology, Metrology, Manufacturing, Electrodes, Data modeling, Capacitance
Currently, CD-SEMs are the tool of choice for in-line gate length measurements for most semiconductor manufacturers. This is in large part due to their flexibility, throughput, and ability to correlate well to physical measurements (e.g., XSEM). However, scatterometry is being used by an increasing number of manufacturers to monitor and control gate lengths. But can a scatterometer measure such small critical dimensions well enough? This paper explores this question by analyzing data taken from wafers processed using 90 nm node technology. These wafers were measured after gate formation (gate final CD) using a CD-SEM as well as a scatterometer. They were then processed into the back-end-of-line and measured electrically. This electrical measurement, called Lpoly, is an important parametric device measurement and is used to screen product before it reaches final electrical test. It is therefore critical for the in-line metrology immediately after gate formation to have excellent correlation to Lpoly. Analysis shows that the scatterometer correlates well to both in-line CD-SEM measurements across multiple structures as well as electrical Lpoly measurements. More importantly, the scatterometer is shown to be approximately equivalent to the CD-SEM when both are correlated to Lpoly. Since several scatterometry targets with different pitches were measured, the amount of correlation as a function of pitch is also investigated. Because traditional methods of correlation, such as Ordinary Least Squares (OLS), have severe limitations, Total Measurement Uncertainty (TMU) analysis is used as a highly effective assessment methodology. This paper also shows how TMU analysis is used to improve the scatterometry model and understand the relative contributions from obstacles that hinder the achievement of even better correlations.
