Voids in copper lines are a common failure mechanism in the back end of line (BEOL) of integrated circuits manufacturing, affecting chip yield and reliability. As subsequent process nodes continue to shrink metal line dimensions, monitoring and control of these voids gain more and more importance . Currently, there is no quantitative in-line metrology technique that allows voids to be identified and measured. This work aims to develop a new method to do so, by combining scatterometry (also referred to as Optical Critical Dimension or Optical CD) and low-energy x-ray fluorescence (LE-XRF), as well as machine learning techniques. By combining the inputs from these tools in the form of hybrid metrology, as well as with the incorporation of machine learning methods, we create a new metric, referred to as <i>V<sub>xo</sub></i>, to characterize the quantity of void. Additionally, the results are compared with inline electrical test data, as higher amounts of voids were expected to increase the measured resistivity. This was not found to be the case, as the impact of the voids was much less of a factor than variation in the cross-sectional area of the lines.
Metrology of nanoscale patterns poses multiple challenges that range from measurement noise, metrology errors, probe size etc. Optical Metrology has gained a lot of significance in the semiconductor industry due to its fast turn around and reliable accuracy, particularly to monitor in-line process variations. Apart from monitoring critical dimension, thickness of films, there are multiple parameters that can be extracted from Optical Metrology models3. Sidewall angles, material compositions etc., can also be modeled to acceptable accuracy. Line edge and Line Width roughness are much sought of metrology following critical dimension and its uniformity, although there has not been much development in them with optical metrology. Scanning Electron Microscopy is still used as a standard metrology technique for assessment of Line Edge and Line Width roughness. In this work we present an assessment of Optical Metrology and its ability to model roughness from a set of structures with intentional jogs to simulate both Line edge and Line width roughness at multiple amplitudes and frequencies. We also present multiple models to represent roughness and extract relevant parameters from Optical metrology. Another critical aspect of optical metrology setup is correlation of measurement to a complementary technique to calibrate models. In this work, we also present comparison of roughness parameters extracted and measured with variation of image processing conditions on a commercially available CD-SEM tool.
Pattern transfer fidelity is always a major challenge for any lithography process and needs continuous improvement. Lithographic processes in semiconductor industry are primarily driven by optical imaging on photosensitive polymeric material (resists). Quality of pattern transfer can be assessed by quantifying multiple parameters such as, feature size uniformity (CD), placement, roughness, sidewall angles etc. Roughness in features primarily corresponds to variation of line edge or line width and has gained considerable significance, particularly due to shrinking feature sizes and variations of features in the same order. This has caused downstream processes (Etch (RIE), Chemical Mechanical Polish (CMP) etc.) to reconsider respective tolerance levels. A very important aspect of this work is relevance of roughness metrology from pattern formation at resist to subsequent processes, particularly electrical validity. A major drawback of current LER/LWR metric (sigma) is its lack of relevance across multiple downstream processes which effects material selection at various unit processes. In this work we present a comprehensive assessment of Line Edge and Line Width Roughness at multiple lithographic transfer processes. To simulate effect of roughness a pattern was designed with periodic jogs on the edges of lines with varying amplitudes and frequencies. There are numerous methodologies proposed to analyze roughness and in this work we apply them to programmed roughness structures to assess each technique’s sensitivity. This work also aims to identify a relevant methodology to quantify roughness with relevance across downstream processes.