Automated image analysis and image classification system that employs machine learning has been developed and applied to the PWQ/FEM flow to enhance the Process Stability Diagnosis (PSD) solution, which can now handle a significant volume of wafer images while realizing a 4X reduction in time to results. Moving the task of image analysis and classification to the computer has the added benefit of avoiding person-to-person inconsistencies in classification. The data flow consists of an automated machine learning-enabled process window analysis system that relies on CDSEM images taken on a FEM or Focus/Exposure Matrix wafer. We report results based on CDSEM images containing both contact hole and line features. The system enables full-wafer SEM image auto-classification and process window characterization.
The importance of pattern-based defect study has grown with more complex processes in advanced semiconductor manufacturing. The pattern is the heart of the DPTCO Design Process Technology Co-Optimization approach. But the definition of pattern has been limited by the design rules that can be setup by an individual. Moreover, the huge volume of data points generated by any DRC Design Rule Check type of search forces user to sort and filter out most of them and keep only a manageable count. This effectively reduces the sample space of pattern-based learning. In this work we have employed a new approach of PCYM Pattern Centric Yield Manager where the high count of unique patterns and all its instances in full chip design is retained. It is a fundamental pillar of computational system for semiconductor fabrication where pattern-centric learning can be deployed to study any related process.
The stability of photolithography process tool is the fundamental to the fabrication of semiconductor devices. Several process control methods are employed to qualify and then monitor every single process layer at the photolithography stage. The CDSEM (Critical Dimension Scanning Electron Microscope) measurements on metrology features and the optical inspection and DRSEM (Defect Review Scanning Electron Microscope) on device features for Process Window Qualification is part of the conventional process control. Here we employed a novel PSD (Process Stability Diagnosis) solution that provides detailed Bossung plot like analysis on device features using CDSEM or DRSEM images. This provides quick insight into the process behavior and also identifies the root cause for any deviation. In this paper we will discuss about monitoring the depth of focus and the best focus as well as diagnosis for lens parameters like astigmatism and spherical aberration. We describe the method of extracting relevant parameters from high resolution images and establishing an automatic monitor for these critical indicators.
Rigorous patterning control at critical wafer process steps of semiconductor fabrication is done to ensure integrity of the manufacturing process. At times, still with the entire existing process control infrastructure, we run into defect issues. Here we report an innovative methodology of Pattern Monitor that complements the existing approach and consistently detects critical defects on wafer that are hard to find using conventional wafer inspection tools. The unique integrated pattern centric approach puts this method apart from all the current inline tools. The Die-to-Database Pattern Monitor (D2DB-PM) solution has been applied to understand the evolution of pattern deformation through process integration engineering. A pattern centric engine is the key to this successful solution that is used to process large volumes of already existing Scanning Electron Microscope (SEM) images to perform Die-to-Database shape and critical dimension evaluation to detect deviations in the patterning behavior. This solution helped to resolve the limited Critical dimension (CD) measurement constraint that is otherwise associated with Critical Dimension Scanning Electron Microscope (CDSEM) measurement. In this paper we report the results from this innovative solution to detect process marginality and also verify the improved patterning behavior after the process fix is implemented.
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