Process control of advanced semiconductor nodes is not only pushing the limits of metrology equipment requirements in terms of resolution and throughput but also in terms of the richness of data to be extracted to enable engineers to finetune the process steps for increased yield. The move towards 3D structures requires extraction of critical dimension parameters from structures which can vary largely from layer to layer. For in-line process control, the necessary automation forces the development of layer and equipment-specific dedicated image processing algorithms. Similarly, with the increase in stochastic defects in the EUV era, detection of defects at the nm scale requires the identification of features captured in low resolution to meet the throughput requirements of HVM fabs, which can again lead to custom algorithm development. With the emergence of ML-based image processing methods, this process of algorithm development for both cases can be accelerated. In this work, we provide the general framework under which the images obtained from high-speed scanning probe microscopy-based systems can be used to train a network for either feature detection for parameter extraction or defect identification.
Advanced semiconductor nodes are pushing the limits of feature sizes and require metrology with sub-nm resolution without compromising on the throughput as needed for in-line process control. Recently, high-throughput scanning probe microscopy (SPM) based metrology and inspection tools capable of meeting these needs have been introduced to the market and qualified for use in HVM. While innovative measurement methods and tool architecture have allowed for a leap of improvement in throughput, the next step in further reducing imaging time can be obtained through the application of machine learning for enhancing the resolution of measured images for extraction of relevant parameters. In this work, we provide the general framework under which a neural network-based resolution enhancer is designed and used for SPM images. We showcase the effectiveness of this framework using measurements performed on Line/Space structures with a pitch of 200 nm. For the reusability of a pre-developed pre-trained model, we additionally leverage transfer learning and show that a new model for slightly differing structures can be re-trained and calibrated with a smaller data set of measurements performed on Line/Space structures with a pitch of 100 nm.
High-NA EUV technology enables cost-effective patterning below the 5nm node. The integration is simpler but still requires multiple innovations. Thinner resists are needed for single-patterning enablement. The decrease in thickness poses a challenge for traditional metrology and inspection systems like OCD or CD-SEM, which lose sensitivity due to diminishing interaction volume. The reverse is true for Scanning Probe Microscopy, which excels in the low-height patterning regime. Here we discuss patterning metrology and introduce defect inspection / review applications for High-NA EUV patterning using a high-throughput SPM.
Improved resolution of the High-NA EUV technology comes with thinner photoresist and smaller aspect-ratio requirements. Trade-offs include more stringent process control needs for resist loss and line roughness. Traditional metrologies like OCD or CD-SEM lose sensitivity due to diminishing interaction volume. A metrology technique that thrives in this regime is Scanning Probe Microscopy: thinner resist allows for higher scanning speed, and smaller aspect ratio for higher measurement accuracy. Here we propose a High-Throughput SPM technique as key enabler for High-NA EUV process control. Detailed, high-density full wafer measurements of resist loss, CD and roughness are enabled by a high-throughput, 4-head SPM toolset, and compared for different resist thicknesses down to 10nm. Sampling schemes consistent with scanner throughput are considered.
The performance of image-based and diffraction-based overlay metrology depends on the quality of optical signal returning from the buried mark. A desirable class of hard mask materials are the metal-based hard masks. The challenge with metal-based hard masks is that they are typically opaque in the visible range. To enable overlay metrology, a costly process integration scheme replaces the opaque material over the overlay target, while another detects residual topography propagating through the film. Here we discuss the progress towards the fully automated Scanning Probe Metrology tool that combines surface and subsurface channel information in a single image to measure overlay through opaque films.
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