Using AI to solve an issue arising with progress in device scaling, will contribute to customer productivity improvement and quality control
Hitachi has developed new AI image analysis technology to improve the accuracy of detecting nanoscale defects that can occur in the semiconductor manufacturing process. In semiconductor defect inspection using SEM,1 the device surface is scanned by an electron beam. Secondary electrons,2 backscattered electrons,3 and other signals are detected to observe the surface shape and defects as images. The number of inspection points increases as the pattern size of devices becomes smaller. When scanning many times to inspect many inspection points over a large area, the scan changes the state of the sample and the collected signals become inconsistent. The brightness distribution of images fluctuates depending on the observed locations. Conventional inspection method4 suffered from accuracy degradation as a result.
Hitachi, combining years of experience in electron microscope technology and digital technologies developed for a wide range of business fields, has developed new AI technology to estimate the brightness distribution at each observed location (Figure 1). Evaluation using the data, which has low accuracy in the conventional method,5 has shown an approximately tenfold improvement in accuracy. With the technology that increases detection accuracy in large area inspection, we aim to contribute productivity improvement and quality control for customers who manufacture semiconductor devices.
Hitachi plans to present this technology at the SPIE Advanced Lithography + Patterning 2024 conference on February 25-29, 2024.
- SEM: Scanning Electron Microscope. Equipment that observes microstructures by scanning the sample surface with a finely focused electron beam in a vacuum environment.
- Secondary electrons: Electrons with an energy of 50 eV or less emitted by the interaction between the electron beam and the sample.
- Backscattered electrons: Electrons with energy greater than 50eV emitted by the interaction between the electron beam and the sample.
- A method that uses AI to estimate brightness distributions of non-defective images from the corresponding design layout, assuming no brightness fluctuation.
- Using Hitachi data with brightness fluctuation, we compared under the condition where the defect detection rate is around 80% as a trade-off with accuracy.
Figure 1. AI technology to estimate the brightness distribution at each observed location on a device
(AI model estimates brightness distributions at each observed location (inspection image 1, inspection image 2) from inspection images and the corresponding design layout. Defect (b) is correctly identified as a defect. While location c has the same brightness as b, the brightness distributions are estimated to be different; accordingly, c is not misidentified as a defect.)
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