Metrology in the Presence of CMOS Under Array (CUA) Structures Utilizing Model-Less Machine Learning

A system comprising:

• an optical characterization system;
• a reference characterization system;
• and
• a controller including one or more processors configured to execute program instructions causing the one or more processors to implement a measurement recipe by:
• receiving optical measurement data for one or more training samples including complementary metal-oxide-semiconductor under array (CuA) devices from the optical characterization system, wherein the CuA devices include CMOS structures disposed beneath periodic memory array structures;
• receiving reference data for the one or more training samples, wherein the reference data includes measurements of geometric parameters of the CuA devices from the reference characterization system;
• training a machine learning model with the optical measurement data for the one or more training samples and the reference data;
• receiving optical measurement data for one or more test samples including CuA devices from the optical characterization system;
• and
• determining one or more measurements of the geometric parameters of the CuA devices on the one or more test samples using the machine learning model with the optical measurement data for the one or more test samples.

A method comprising:

• generating optical measurement data for one or more training samples including complementary metal-oxide-semiconductor (CMOS) under array (CuA) devices, wherein the CuA devices include CMOS structures disposed beneath periodic memory array structures;
• generating reference data for the one or more training samples, wherein the reference data includes measurements of geometric parameters of the CuA devices;
• training a machine learning model with the optical measurement data for the one or more training samples and the reference data;
• generating optical measurement data for one or more test samples including CuA devices;
• and
• determining one or more measurements of the geometric parameters of the CuA devices on the one or more test samples using the machine learning model with the optical measurement data for the one or more test samples.