US 2026/0289057 A1Published application

Scalable and dynamic multi-modal chip design hotspot classification

AssigneeApplied Materials, Inc.

InventorsSriram MADHAVAN, Dastagiri DUDEKULA, Tianchi LIU, Martinus Maria BERKENS

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Abstract

Identifying hotspots, or regions on the semiconductor designer substrate that are prone to manufacturing defects or performance issues, requires either error-prone techniques like design rule checking, or resource-intensive techniques such as scanning electron microscopy (SEM) imaging. This disclosure describes training and using a model-based architecture to quickly identify a probability of these hotspots at specific locations. The model is scalable and multi-modal, using inputs of different types (images, tabular, timeseries, etc.) from different sources. Detecting the likelihood of a hotspot can be done very efficiently with this model-based solution and can be used to minimize false positives and negatives before employing more time-intensive inspection techniques.

Claims20

Dependent claims

A method for predicting hotspots on semiconductor substrates, the method comprising:

receiving first data associated with a manufacturing a semiconductor substrate, wherein the first data comprises a first data type, and the first data is associated with a location on the semiconductor substrate;
receiving second data associated with the manufacturing of the semiconductor substrate, wherein the second data comprises a second data type that is different from the first data type, and the second data is associated with the location on the semiconductor substrate;
providing the first data and the second data to a trained machine-learning model, wherein the trained machine-learning model comprises:
a first neural network configured to process data of the first data type;
a second neural network configured to process data of the second data type;
and
an output layer that generates a likelihood of a hotspot at the location on the semiconductor substrate based at least in part on outputs of the first neural network and the second neural network;
and
causing an inspection process to be performed at the location on the semiconductor substrate based at least in part on the output of the trained machine-learning model.

6 more elements

A method of training machine-learning models to generate likelihoods of hotspots at locations on semiconductor substrates, the method comprising:

defining a plurality of configurations for a machine-learning model, wherein each of the plurality of configurations define:
first parameters for a first neural network configured to process first data of a first data type, wherein the first data is associated with a manufacturing of a semiconductor substrate and associated with a location on the semiconductor substrate;
second parameters for a second neural network configured to process second data of a second data type that is different from the first data type, wherein the second data is associated with the manufacturing of the semiconductor substrate and associated with a location on the semiconductor substrate;
and
connections between components of the machine-learning model including the first neural network and the second neural network;
training instances of the machine-learning model using each of the plurality of configurations to generate likelihoods of a hotspot at the location on the semiconductor substrate;
identifying a configuration in the plurality of configurations that best determines likelihoods of hotspots on semiconductor substrates;
and
using the machine-learning model with the configuration to determine the likelihoods of hotspots on semiconductor substrates.

6 more elements

A system comprising:

one or more processors;
and
one or more memory devices storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving first data associated with manufacturing a semiconductor substrate, wherein the first data comprises a first data type, and the first data is associated with a location on the semiconductor substrate;
receiving second data associated with the manufacturing of the semiconductor substrate, wherein the second data comprises a second data type that is different from the first data type, and the second data is associated with the location on the semiconductor substrate;
providing the first data and the second data to a trained machine-learning model, wherein the trained machine-learning model comprises:
a first neural network configured to process data of the first data type;
a second neural network configured to process data of the second data type;
and
an output layer that generates a likelihood of a hotspot at the location on the semiconductor substrate based at least in part on outputs of the first neural network and the second neural network;
and
determining the likelihood of the hotspot at the location on the semiconductor substrate based at least in part on an output of the trained the machine-learning model.

9 more elements

Dependencies are inferred from the claim text.