Differentiable IC Layout Manufacturability for Gradient Optimization
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Solution Overview
Problem
Current integrated circuit design methods rely on binary design rules that do not account for the continuous and probabilistic nature of manufacturability constraints, making it difficult to optimize designs for manufacturability and yield without gradient-based methods.
Innovation Solution
Developing differentiable manufacturability models using machine learning techniques that incorporate physical simulations and yield data to provide continuous and differentiable manufacturability parameters, enabling holistic optimization of integrated circuit layouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If binary design rules are used for manufacturability checking, then compliance verification is simplified, but optimization capability for manufacturability and yield is lost
Solution Approach 1:
The patent transforms the binary manufacturability parameter into a continuous differentiable parameter that ranges from 0 to 1, where 0 represents non-compliant and 1 represents compliant. This parameter transformation enables gradient-based optimization methods to be applied while maintaining the essential compliance verification functionality.
Solution Approach 2:
The patent introduces a differentiable manufacturability model as an intermediary between the binary design rules and the optimization algorithm. This model acts as a bridge that translates discrete compliance checks into continuous gradients, enabling end-to-end optimization of circuit layouts for manufacturability and yield.
2Measurement precision
If detailed manufacturability data is retained by foundries, then accuracy of manufacturability assessment is improved, but accessibility for designers is reduced
Solution Approach 1:
The patent creates a differentiable copy of the manufacturability assessment model that replicates the functionality of the foundry's detailed manufacturability data. This copied model enables designers to perform gradient-based optimization locally without direct access to the proprietary foundry data, while maintaining accuracy through careful reproduction of the underlying physical constraints.
3Device complexity
If Boolean design rules are used, then compliance determination is simplified, but continuous optimization for yield improvement is prevented
Solution Approach 1:
The patent changes the parameter type from Boolean (discrete) to continuous differentiable, transforming the compliance metric into a gradient-friendly format. This allows optimization algorithms to continuously adjust design parameters to maximize yield while respecting manufacturability constraints, rather than being limited to discrete compliance checks.
Solution Approach 2:
The patent replaces the mechanical Boolean logic system with a differentiable computational model that supports gradient-based optimization. This substitution enables the use of powerful optimization techniques from machine learning and continuous mathematics to improve yield, while maintaining the essential constraint enforcement functionality.
Data Source
AI summary
Systems, computer-implemented methods, and instructions encoded in machine-accessible storage media are provided for determining manufacturability of an integrated circuit layout. A computer-implemented method includes receiving a layout describing the integrated circuit to be manufactured by a semiconductor manufacturing process. The method also includes generating a differentiable manufacturability parameter as an output of a machine learning model using the layout, the machine learning model being trained to generate the differentiable manufacturability parameter. The differentiable manufacturability parameter describes the manufacturability of the integrated circuit by the semiconductor manufacturing process.