Dmitriy Shneyder

Comparison of scatterometry, atomic force microscope, dual beam system, and XSEM to measure etched via depths

Open vias can be a significant yield loss due to the difficulty in detecting them. In-line monitoring of via depths on product wafers is one approach to minimize this problem. Atomic force microscopes (AFMs) have been the primary choice for this, but using the AFM to measure the depths of the high aspect ratio vias of today’s advanced chips is extremely difficult in production. Scatterometers are another in-line choice for measuring product wafers, but the many underlayers and 3D nature of vias make implementation of this metrology challenging and, so far, largely untested. A different way to detect open vias is through the use of patterned etch tool monitor wafers containing a thick layer of inter-level dielectric (ILD). Destructive XSEM measurements can be used to measure via depths on such wafers. But between transfer of the wafer from the fab to the lab, sample preparation time, imaging time, and communication of the final results from the lab back to the fab, several hours are lost. During this time the etch tool is not qualified to process wafers, and so productivity is reduced. Because of this long delay and the cost of performing XSEMs, three in-line methods were evaluated as potential candidates to replace XSEM metrology. These methods used state-of-the-art equipment and included scatterometry, AFM, and a dual beam system (a combination of an SEM and a Focused Ion Beam (FIB)). A new type of Reference Measurement System (RMS), combining results from multiple measurement systems, is introduced. This new method is used to evaluate the quality of the results from the different systems. Results show that the scatterometer, AFM, and dual beam system performed well. XSEM metrology was found to be more inaccurate than expected at measuring via depths.

Monitoring measurement tools: new methods for driving continuous improvements in fleet measurement uncertainty

Ever shrinking measurement uncertainty requirements are difficult to achieve for a typical metrology toolset, especially over the entire expected life of the fleet. Many times, acceptable performance can be demonstrated during brief evaluation periods on a tool or two in the fleet. Over time and across the rest of the fleet, the most demanding processes often have measurement uncertainty concerns that prevent optimal process control, thereby limiting premium part yield, especially on the most aggressive technology nodes. Current metrology statistical process control (SPC) monitoring techniques focus on maintaining the performance of the fleet where toolset control chart limits are derived from a stable time period. These tools are prevented from measuring product when a statistical deviation is detected. Lastly, these charts are primarily concerned with daily fluctuations and do not consider the overall measurement uncertainty. It is possible that the control charts implemented for a given toolset suggest a healthy fleet while many of these demanding processes continue to suffer measurement uncertainty issues. This is especially true when extendibility is expected in a given generation of toolset. With this said, there is a need to continually improve the measurement uncertainty of the fleet until it can no longer meet the needed requirements at which point new technology needs to be entertained. This paper explores new methods in analyzing existing SPC monitor data to assess the measurement performance of the fleet and look for opportunities to drive improvements. Long term monitor data from a fleet of overlay and scatterometry tools will be analyzed. The paper also discusses using other methods besides SPC monitors to ensure the fleet stays matched; a set of SPC monitors provides a good baseline of fleet stability but it cannot represent all measurement scenarios happening in product recipes. The analyses presented deal with measurement uncertainty on non-measurement altering metrology toolsets such as scatterometry, overlay, atomic force microscopy (AFM) or thin film tools. The challenges associated with monitoring toolsets that damage the sample such as the CD-SEMs will also be discussed. This paper also explores improving the monitoring strategy through better sampling and monitor selection. The industry also needs to converge regarding the metrics used to describe the matching component of measurement uncertainty so that a unified approach is reached regarding how to best drive the much needed improvements. In conclusion, there will be a discussion on automating these new methods3,4 so they can complement the existing methods to provide a better method and system for controlling and driving matching improvements in the fleet.