Overlay is one of the critical parameters and directly impacts yield. Due to high metrology cost, only a small number of wafers are measured per lot. To this end, virtual metrology (VM) aims to provide valuable information about the nonmeasured wafers with little to no additional cost. VM leverages historical per-wafer measurements from exposure tools and processing equipment collected at previous process steps to report overlay on every wafer. As data-driven approaches gain more adoption in the semiconductor manufacturing, machine learning (ML) is a natural choice to tackle this task. In this paper, we present the strategies of learning overlay prediction models from exposure and process context data as well as the steps for achieving desired prediction performance, including data preparation, feature selection, best modeling methods, hyperparameters tuning and objective. We demonstrate our methodology on a large HVM dataset under stable APC conditions.
For advanced lithography metrology, SCD (Scatterometry Critical Dimension) is a common metrology
technique applied to control processes. SCD has the capability to report accurate data information such as
CD (Critical Dimensions), photoresist SWA (Side Wall Angle) and photoresist HT (Height). The shape of
photoresist correlates with inline process controllers, namely scanner focus and dose. However, SCD is a
model-based metrology method. In order to decode the process controllers, it requires computation from a
geometric model. Once the model extracts the resist shape information from the spectra, one needs further
correlation of those geometric parameters with the process controllers for monitoring. Thus, information
loss through multiple modeling is a major concern. Indeed, during data transformation, noise and model
approximation can distort the signals, in other words, the critical parameters, focus and dose, may not be
measured accurately.
This study therefore seeks a methodology to monitor focus and dose with the least amount of information
transformation. Signal Response Metrology is a new measurement technique that obviates the need for
geometric modeling by directly correlating focus, dose or CD to the spectral response of a SCD-based
metrology tool.
In advanced semiconductor N14 processes, due to the requirement of shrinking pitches for increased densities, pattern split is introduced. However, each of the two pattern split methods, SADP (Self-Aligned Double Patterning) and LELE (Litho-Etch-Litho-Etch), can incur process variations that might cause “pitch walk” [1, 3]. Pitch walk is a by-product of line critical dimension (CD) and spacer error (in SADP) or overlay variations (in LELE). Pitch walk not only results in different line and spaces but also affect the later steps, for example, different etched depths due to loading effects. Because of those behaviors, it is therefore a requirement to control the CD for better uniformity. This paper demonstrates how to use SCD (Scatterometry Critical Dimension) metrology tools to measure the different critical dimensions and spaces to control CD and overlay at the same process step [2]. Traditionally, wafers have to go through both a CD metrology tool and an overlay tool in order to verify CD uniformity and grid uniformity. The methodology introduced in this paper can efficiently shorten cycle time since only the CD metrology tool will be used to verify both CD and overlay. SpectraShape™ is a proven optical CD platform based on spectroscopic ellipsometry and reflectometry. In optical model type metrology, pitch walk can be a challenging parameter to measure due to inherent low sensitivity. Hence this study is performed on the newest generation system, the SpectraShape™ 9010. A new, laser driven light source [4] on this SCD tool provides higher light intensity, producing better signal-to-noise ratios for critical device parameters. This paper explores the use of SCD to measure both resist and hard mask CD in a single step. In addition, results will be presented on using SCD to measure pitch walk, typically a low sensitivity parameter for optical CD metrology tools.
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