Kla Tencor Corporation

Kla Corporation

Hari Pathangi

Eran Amit

Nadav Gutman

Frank Laske

Stefan Eyring

Arpit Yati

Ulrich Pohlmann

Jagdish Chandra Saraswatula

Sivaprrasath Meenakshisundaram

Tanay Bansal

Thomas Heidrich

Sandeep Madhogarhia

Rohit Bhat

During electron beam imaging of a semiconductor wafer, the electron beam is adjusted to a first electron dose/nm/time value below a damage threshold for an image frame grab of a site on the semiconductor wafer. Then the electron beam is adjusted to a second electron dose/nm/time value different from the first electron dose/nm/time value for a second image frame grab of the site. The second electron dose/nm/time value can be above the damage threshold.

Enabling scanning electron microscope imaging while preventing sample damage on sensitive layers used in semiconductor manufacturing processes

A semiconductor review tool receives absolute Z-height values for the semiconductor wafer, such as a semiconductor wafer with a beveled edge. The absolute Z-height values can be determined by a semiconductor inspection tool. The semiconductor review tool reviews the semiconductor wafer within a Z-height based on the absolute Z-height values. Focus can be adjusted to within the Z-height.

Using absolute Z-height values for synergy between tools

Systems and methods for determining location of critical dimension (CD) measurement or inspection are disclosed. Real-time selection of locations to take critical dimension measurements based on potential impact of critical dimension variations at the locations can be performed. The design of a semiconductor device also can be used to predict locations that may be impacted by critical dimension variations. Based on an ordered location list, which can include ranking or criticality, critical dimension can be measured at selected locations. Results can be used to refine a critical dimension location prediction model.

Inspection-guided critical site selection for critical dimension measurement

A heat map of probable defects in an image can be represented as a matrix of defect probability index corresponding to each pixel. The image may be generated from data received from a detector of a scanning electron microscope or other inspection tools. A number of pixels in the image that exceed a corresponding threshold in the matrix can be quantified.

Using deep learning based defect detection and classification schemes for pixel level image quantification

An overlay metrology system includes a particle-beam metrology tool to scan a particle beam across an overlay target on a sample including a first-layer target element and a second-layer target element. The overlay metrology system may further include a controller to receive a scan signal from the particle-beam metrology tool, determine symmetry measurements for the scan signal with respect to symmetry metrics, and generate an overlay measurement between the first layer and the second layer based on the symmetry measurements in which an asymmetry of the scan signal is indicative of a misalignment of the second-layer target element with respect to the first-layer target element and a value of the overlay measurement is based on the symmetry measurements.

Overlay measurements of overlapping target structures based on symmetry of scanning electron beam signals

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Chennai, India

Hari Pathangi