GAN-Based Multi-Layer Layout Pattern Generation
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Solution Overview
Problem
Current methods for generating multi-layer integrated physical design layout patterns in semiconductor manufacturing are limited, as existing Electronic Design Automation (EDA) tools rely on predefined building blocks and hardcoded rules, making it cumbersome to create integrated layout patterns for multi-layer structures, and manual scripting is error-prone and lacks scalability.
Innovation Solution
The use of generative adversarial networks (GANs) to automatically generate synthetic multi-layer integrated physical design layout patterns by converting physical design layouts into three-dimensional arrays and training a GAN comprising a discriminator and generator neural network, allowing for the production of realistic patterns that expand pattern libraries and reduce human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If EDA tools use predefined building blocks and hardcoded rules to generate multi-layer integrated physical design layout patterns, then the generation process follows strict design rules, but the process becomes cumbersome and lacks scalability
Solution Approach 1:
The patent replaces the mechanical system of predefined building blocks and hardcoded rules with a neural network-based generative model. The GAN learns design rule constraints implicitly from training data, substituting explicit rule-based generation with learning-based generation that maintains compliance while reducing process complexity
Solution Approach 2:
The patent transforms the generation approach from using fixed building blocks to using continuous latent space representations. By sampling from the latent space and applying transformations, the system generates diverse layout patterns while maintaining design rule compliance, changing the parameter space from discrete blocks to continuous geometric transformations
2Adaptability or versatility
If manual scripting is used to create integrated layout patterns for multi-layer structures, then flexibility in pattern creation is achieved, but the process becomes error-prone and lacks scalability
Solution Approach 1:
The patent implements self-service through the GAN's ability to automatically learn design rules and generation patterns from training data. The system performs self-correction and validation by comparing generated patterns against learned constraints, reducing manual intervention and associated errors while maintaining flexibility
Solution Approach 2:
The patent incorporates feedback mechanisms where the discriminator network evaluates generated patterns and provides gradient feedback to the generator. This closed-loop feedback ensures design rule compliance while maintaining pattern diversity, automatically correcting errors without manual scripting
3Ease of manufacture
If existing methods are used to generate multi-layer integrated physical design layout patterns, then the process follows established workflows, but automation is limited and human intervention is required
Solution Approach 1:
The patent applies preliminary action by pre-training the GAN on extensive layout pattern data that encodes design rules and manufacturing constraints. This preliminary learning phase automates the acquisition of domain knowledge, enabling the system to autonomously generate compliant patterns without requiring human experts to manually encode workflows
Solution Approach 2:
The patent achieves universality by designing a GAN framework that can generate layout patterns for multiple layers and different design styles simultaneously. The single model learns to handle various pattern types and layer configurations, replacing the need for separate specialized tools and workflows for each case
Data Source
AI summary
A method for generating physical design layout patterns includes selecting as training data one or more physical design layout patterns of integrated multi-layers for features in at least two layers of a given patterned structure. The method also includes converting the physical design layout patterns into three-dimensional arrays, a given three-dimensional array comprising a set of two-dimensional arrays each representing features of one layer of the layers in a given one of the physical design layout patterns. The method further includes training, utilizing the three-dimensional arrays, a generative adversarial network (GAN) comprising a discriminator neural network and a generator neural network. The method further includes generating synthetic three-dimensional arrays utilizing the generator neural network of the trained GAN, a given synthetic three-dimensional array comprising a set of two-dimensional arrays each representing features for a new layer of a new physical design layout pattern.