Tokyo Electron Limited

Toshihiro Kitao

Jun Shinagawa

Megan Wooley

Alok Ranjan

Carlos A Fonseca

Hiroshi Nagahata

Chungjong Lee

Atsushi Suzuki

Aspects of the disclosure provide a method for wafer result prediction. The method includes determining predictor parameters of a semiconductor process using domain knowledge that includes knowledge of the semiconductor process, a processing tool associated with the semiconductor process, a metrology tool, and/or the wafer. The method also includes removing collinearity among the predictor parameters to obtain key predictor parameters, and selecting a subset of the key predictor parameters based on metrology data of the wafer obtained from the metrology tool. The method further includes building a virtual metrology (VM) model on the subset of the key predictor parameters and may include predicting wafer results using the VM model.

Virtual metrology for wafer result prediction

A method of optimizing a recipe for a plasma process includes (a) building a virtual metrology (VM) model that predicts a wafer characteristic resulting from the plasma process based on a plasma parameter and (b) building a control model that describes a relationship between the plasma parameter and a recipe parameter. (c) The wafer characteristic is measured after performing the plasma process according to the recipe. (d) Whether the wafer characteristic is within a predetermined range is determined. (e) The VM model and the control model are calibrated based on the wafer characteristic. (f) The recipe is optimized by updating the plasma parameter based on the wafer characteristic using the VM model and updating the recipe parameter based on the plasma parameter using the control model. (c), (d), (e) and (f) are repeated until the wafer characteristic is within the predetermined range.

Virtual metrology enhanced plasma process optimization method

A method of evaluating tool health of a plasma tool is provided. The method includes providing a virtual metrology (VM) model that predicts a wafer characteristic based on parameters measured by module sensors and in-situ sensors of the plasma tool. A classification model is provided that identifies a plurality of failure modes of the plasma tool. An initial test is performed on an incoming wafer to determine whether the incoming wafer meets a preset requirement. The wafer characteristic is predicted using the VM model when the incoming wafer meets the preset requirement. A current failure mode is identified using the classification model when the wafer characteristic predicted by using the VM model is outside a pre-determined range.

Tool health monitoring and classifications with virtual metrology and incoming wafer monitoring enhancements

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Austin, TX, United States of America

Toshihiro Kitao