JP-7855749-B2 - System and method for determining target feature focus in image-based overlay measurements

JP7855749B2JP 7855749 B2JP7855749 B2JP 7855749B2JP-7855749-B2

Inventors

ラバートエタイ
マナッセンアムノン
シモンヨッシ
サンコディミトリー
サフラニアブナー

Assignees

ケーエルエーコーポレイション

Dates

Publication Date: 20260508
Application Date: 20250226
Priority Date: 20201001

Claims (20)

It is a system, A controller communicatively coupled to one or more through-focus imaging measurement subsystems, the controller comprising one or more processors configured to execute a set of program instructions stored in memory, the set of program instructions is configured to execute the one or more processors The steps include receiving one or more training images, which are multiple training images captured at one or more focal positions and which include one or more training features of a training sample, The steps include generating a machine learning classifier based on multiple training images captured at one or more focal positions, The steps include receiving one or more target feature selections, which are user-selected target features, for one or more target overlay measurements corresponding to one or more target features of a target sample, The steps include: determining one or more target focus locations based on the selection of one or more target features using the machine learning classifier; Steps include receiving one or more target images captured at one or more target focal positions, wherein the one or more target images include one or more target features of a target sample, The steps include determining one or more overlays based on the one or more target images, A system characterized by having a controller configured to perform the following actions.
The system according to claim 1, characterized in that the one or more through-focus imaging measurement subsystems comprise at least one of an optical-based measurement subsystem or a scattering-based measurement subsystem.
The system according to claim 1, characterized in that the one or more focal positions include multiple focal positions within the focus training range.
The system according to claim 1, characterized in that the multiple training images are captured at one or more focal positions by translating one or more parts of the measurement subsystem along one or more adjustment axes.
The system according to claim 4, wherein the set of program instructions provides one or more control signals to one or more processors to one or more parts of one or more measurement subsystems, and the one or more control signals are further configured to move one or more parts of one or more measurement subsystems in parallel along one or more adjustment axes.
The system according to claim 5, characterized in that the one or more control signals are configured to center one or more portions of the one or more through-focus imaging measurement subsystems.
The step of determining one or more overlays based on one or more target images of one or more target features is: The system according to claim 1, comprising the step of determining the overlay between the first layer of the target sample and the second layer of the target sample based on a first overlay measurement corresponding to one or more target features formed on the first layer of the target sample and a second overlay measurement corresponding to one or more target features formed on the second layer of the target sample.
The system according to claim 1, characterized in that the selection of one or more target features for one or more overlay measurements is provided by the user via a user interface.
The system according to claim 1, characterized in that the set of program instructions is further configured to provide one or more control signals to one or more process tools for one or more processors.
The system according to claim 1, characterized in that the machine learning classifier comprises at least one of the following: a deep learning classifier, a convolutional neural network, an ensemble learning classifier, a random forest classifier, or an artificial neural network.
It is a system, One or more through-focus imaging and measurement subsystems, A controller communicatively coupled to one or more through-focus imaging measurement subsystems, comprising one or more processors configured to execute a set of program instructions stored in memory, wherein the set of program instructions is configured to execute the one or more processors. The steps include receiving one or more training images, which are multiple training images captured at one or more focal positions and which include one or more training features of a training sample, The steps include generating a machine learning classifier based on multiple training images captured at one or more focal positions, A step of receiving one or more target feature selections, which are user-selected target features, for one or more target overlay measurements corresponding to one or more target features of a target sample; A step of determining one or more target focus locations based on the selection of one or more target features using a machine learning classifier, Steps include receiving one or more target images captured at one or more target focal positions, wherein one or more target images include one or more target features of a target sample; A step of determining one or more overlays based on one or more target images, A system characterized by being configured to perform the following action.
An overlay measurement method using one or more through-focus imaging measurement subsystems, A step of receiving multiple training images captured at one or more focal positions, wherein one or more training images include one or more training features of a training sample, The steps include generating a machine learning classifier based on the plurality of training images captured at one or more focal positions, The steps include receiving one or more target feature selections, which are user-selected target features, for one or more target overlay measurements corresponding to one or more target features of a target sample, The steps include: determining one or more target focus locations based on the selection of one or more target features using the machine learning classifier; A step of receiving one or more target images captured at one or more target focal positions, wherein the one or more target images include one or more target features of a target sample. The steps include determining one or more overlays based on the one or more target images, A method that includes this.
The method according to 12, characterized in that the plurality of training images and the one or more target images are captured by one or more through-focus imaging measurement subsystems, each including at least one of an optical-based measurement subsystem or a scattering-based measurement subsystem.
The method according to 12, characterized in that the one or more focal positions include multiple focal positions within the focus training range.
The method according to 13, characterized in that the plurality of training images are captured at one or more focal positions by translating one or more parts of the measurement subsystem along one or more adjustment axes.
The method according to claim 15, further comprising providing one or more control signals to one or more parts of the one or more through-focus imaging measurement subsystems, wherein the one or more control signals are configured to move one or more parts of the one or more through-focus imaging measurement subsystems in parallel along one or more adjustment axes.
The method according to 16, characterized in that the one or more control signals are configured to center one or more portions of the one or more through-focus imaging measurement subsystems.
The step of determining one or more overlays based on one or more target images of one or more target features is: The method according to 12, comprising the step of determining the overlay between the first layer of the target sample and the second layer of the target sample based on a first overlay measurement corresponding to one or more target features formed on a first layer of the target sample and a second overlay measurement corresponding to one or more target features formed on a second layer of the target sample.
The method according to 12, characterized in that the selection of one or more target features for one or more overlay measurements is provided by the user via a user interface.
The method according to claim 12, further comprising the step of providing one or more control signals to one or more process tools.

Description

This disclosure generally relates to overlay measurement, and more specifically, to machine learning for target feature focusing. Image-based overlay measurements typically involve determining the relative offset between two or more layers on a sample based on the relative imaged positions of overlay target features in different layers of interest. The accuracy of the overlay measurement can therefore be sensitive to the image quality associated with the imaged features on each sample layer, which can vary based on factors such as the depth of field or position of the plane relative to the sample (e.g., focal point). Thus, overlay measurement procedures typically involve a trade-off between image quality and throughput in a particular sample layer. For example, an overlay measurement based on separate images of each sample layer may provide the highest quality image of the overlay target feature. However, capturing multiple images for each target can reduce throughput. As another example, an overlay measurement based on a single image-capture feature across multiple layers may offer relatively high throughput, but may require external tools or a reference measurement based on full wafer measurement to provide the desired measurement accuracy. U.S. Patent Application Publication No. 2016/0025650U.S. Patent Application Publication No. 2018/0191948 Many of the advantages of this disclosure can be better understood by those skilled in the art by referring to the accompanying drawings. This is a conceptual diagram showing a measurement system according to one or more embodiments of the present disclosure. This is a simplified schematic diagram illustrating a measurement system according to one or more embodiments of the present disclosure. This flowchart illustrates the steps performed in a method for measuring an overlay according to one or more embodiments of the present disclosure. This flowchart illustrates the steps performed in a method for measuring an overlay according to one or more embodiments of the present disclosure. Hereinafter, we will refer in detail to the disclosed subject matter shown in the accompanying drawings. This disclosure has been specifically shown and described with respect to particular embodiments and their particular features. The embodiments described herein are to be construed as illustrative, not restrictive. It should be readily apparent to those skilled in the art that various changes and modifications in form and detail can be made without departing from the spirit and scope of this disclosure. Hereinafter, we will refer in detail to the disclosed subject matter shown in the accompanying drawings. Embodiments of this disclosure relate to through-focus imaging systems and methods for overlay targets on a sample, for providing self-reference overlay measurement recipes for additional overlay targets on a sample, as well as inter-wafer process monitoring. Semiconductor devices are typically formed as multiple patterned layers of patterned material on a substrate. Each patterned layer may be fabricated through a series of process steps, including, but not limited to, one or more material deposition steps, one or more lithography steps, or one or more etching steps. Furthermore, the features within each patterned layer must typically be fabricated within selected tolerances to properly construct the final device. For example, overlay errors related to the relative misalignment of features on different sample layers must be well characterized and controlled within each layer and with respect to previously fabricated layers. Therefore, overlay targets can be fabricated on one or more sample layers to enable efficient characterization of interlayer feature overlays. For example, an overlay target may include features fabricated on multiple layers arranged to facilitate accurate overlay measurements. In this regard, overlay measurements on one or more overlay targets distributed across a sample can be used to determine the overlays of corresponding device features associated with the fabricated semiconductor device. Image-based overlay measurement tools typically capture one or more images of an overlay target and determine the overlay between sample layers based on the relative positions of the captured features of the overlay target on the layer of interest. For example, the features of an overlay target suitable for image-based overlays located on different sample layers (e.g., box-in-box targets, advanced imaging measurement (AIM) targets, etc.) may, but do not necessarily, be positioned so that features on all layers of interest are visible simultaneously. In this regard, the overlay can be determined based on the relative positions of features on the layer of interest within one or more images of the overlay target. Furthermore, the overlay target may be designed to facilitate overlay measurement between any number of sample layers in either a single measurement step or multipl