Realize 193nm inspection for EUV mask

With EUV reticle features shrinking and becoming more complicated, conventional 193 nm inspection tools pose significant challenges due to poor signal-to-noise (SNR) ratio, low optical image stability, and limited data processing capability. To meet these challenges, we have implemented machine learning techniques in EUV reticle inspection starting from advanced technology nodes, which effectively improve defect SNR and eliminates false counts. This accomplishment has been made possible through a combination of several innovations addressed in KLA's third generation Teron^™ 640e Series system: 1. Aberration Control Compensation Techniques: These techniques reduce intrinsic optical noise, enhancing accurate defect detection. 2. Focus drift improvement: Controlling focus drift within tens of nanometers through a full mask area scan is achieved by deploying high-frequency focus trajectory calibration and a low thermal expansion stage. 3. X30 Die-to-Database (DB) Inspection Mode: Leveraging Gen-2 deep learning algorithms, this encompasses a comprehensive analysis of layout dimensions and the integration of design elements through to the final pattern generation. The objective is to enhance the modeling process, thereby diminishing noise levels for superior inspection sensitivity. 4. Curvilinear-Friendly Geometry Classification Scheme: KLA-designed Gen-2 feature map for advanced inspection sensitivity control. 5. Enhanced Data Preparation Server: Efficiently handling data sizes of OASIS P49 MULTIGON format four times larger than traditional Manhattan formats, this server ensures comparable data preparation time. The 3rd generation Teron^™ 640e Series system has been demonstrated to meet production requirements for N technology node and beyond. The next step will focus on cutting-edge optical and algorithm design to overcome resolution limitations and implementing these advanced technologies in the most suitable areas of EUV mask inspection.