Semiconductor Pattern Generation with Alternating Restoration
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Solution Overview
Problem
Existing semiconductor pattern transfer processes, such as photolithography and etching, suffer from optical proximity effects and loading effects, leading to inaccuracies in transferring circuit patterns from masks to wafers.
Innovation Solution
A processor-implemented method using a generative model with alternating restoration processes, including denoising and noise addition, to refine semiconductor patterns through multiple sequences of restorative operations based on similarity measurements and variable time step lengths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional photolithography and etching processes are used for pattern transfer, then the manufacturing process is simple and well-established, but manufacturing precision deteriorates due to optical proximity effects and loading effects causing gaps or differences between transferred patterns and original designs
Solution Approach 1:
The generative model performs preliminary restorative operations on the input image before final pattern generation. Multiple sequences of restorative operations are executed in advance to predict and correct pattern deformations, including optical proximity effects and loading effects, before the actual semiconductor manufacturing process begins.
Solution Approach 2:
The method uses generative model copying to create multiple restored versions of the input image through different sequences of restorative operations. These copied and restored images are then compared and synthesized to produce a final pattern that compensates for anticipated manufacturing deformations, achieving higher precision without changing the physical manufacturing process.
2Manufacturing precision
If multiple sequences of restorative operations are performed to improve pattern accuracy, then manufacturing precision improves, but loss of time increases due to multiple processing sequences
Solution Approach 1:
The method employs periodic execution of restorative operations through multiple sequences, where the generative model alternates between different restoration processes. This periodic application of restorative operations allows the system to converge on an accurate final pattern while managing computational time through structured repetition.
Solution Approach 2:
The generative model maintains continuous useful action by executing multiple sequences of restorative operations without interruption. Each sequence builds upon the previous one, with the model continuously refining the pattern prediction until convergence, ensuring that computational resources are consistently applied toward the goal of accurate pattern generation.
3Manufacturing precision
If generative model with multiple restoration sequences is used, then pattern accuracy and contour clarity improve, but device complexity increases due to multiple restoration processes and similarity measurements
Solution Approach 1:
The restoration process is segmented into multiple distinct sequences, each handling specific aspects of pattern restoration. The generative model divides the complex restoration task into manageable sequences that can be executed independently and then combined, making the overall complex process more controllable and systematic.
Solution Approach 2:
The method implements feedback mechanisms by measuring similarity between images generated from different sequences of restorative operations. This similarity measurement provides feedback to the system, allowing it to evaluate the effectiveness of each restoration sequence and synthesize the final pattern based on this feedback, thereby improving contour clarity through iterative refinement.
Data Source
AI summary
A method with semiconductor pattern generation includes generating a first image by performing a first sequence of restorative operations, of a generative model that is initially provided an input image, based on a first set restoration process, wherein the generative model is a circuitry pattern-based generative model having a plurality of restorative operations, generating a second image by performing a second sequence of restorative operations, of the generative model and continuing from the first image, based on the first set restoration process, and generating, dependent on a determined similarity between the first image and the second image, a final semiconductor pattern by performing multiple restorative operations, of the generative model continuing from the second image, that include a third sequence of restorative operations of the generative model based on a second set restoration process that is different from the first set restoration process.