Reinforcement Learning Standard Cell Placement
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Solution Overview
Problem
Advanced technology nodes in semiconductor manufacturing face challenges in generating standard cell layouts due to the complexity and number of design rule constraints (DRCs), which existing mathematical optimization methods struggle to efficiently handle, particularly in automating standard cell layout design and routing processes.
Innovation Solution
A reinforcement learning-based device placement algorithm and a genetic routing algorithm with reinforcement learning for DRC correction are employed to generate placements and routes that adhere to design rules, decoupling routing and DRC fixing to improve scalability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If mathematical optimization methods (SAT, MILP) are used to generate standard cell layouts, then placement solutions can be found under imposed constraints, but the complexity and number of design rule constraints (DRCs) make the process inefficient and difficult to scale
Solution Approach 1:
The patent segments the routing problem into two independent operations: placement and routing. Placement locates devices and assigns pin locations without needing to satisfy all DRCs simultaneously. Routing then connects device terminals and pins while handling DRCs. This segmentation allows each operation to be optimized independently, improving overall design speed while maintaining placement accuracy through the placement operation.
Solution Approach 2:
The patent introduces reinforcement learning as an intermediary between design requirements and layout generation. The reinforcement learning model learns to generate placements and routing solutions that satisfy complex DRCs through iterative training, acting as a mediator that translates design rules into actionable placement and routing decisions without requiring manual expression of all constraints in optimization frameworks.
2Productivity
If deterministic channel routing methods are used, then routing solutions can be generated quickly, but they do not handle design rule constraints (DRCs) well and cannot find routing solutions for complicated cells
Solution Approach 1:
The patent employs reinforcement learning with feedback mechanisms where the routing operation receives feedback about DRC compliance and adjusts subsequent routing decisions accordingly. The system learns from successful and unsuccessful routing attempts, using this feedback to improve DRC compliance while maintaining routing speed, solving the contradiction between fast deterministic routing and proper DRC handling.
Solution Approach 2:
The patent makes the routing process dynamic by using reinforcement learning to adapt routing strategies based on learned patterns and experiences. Rather than following fixed deterministic rules, the routing operation dynamically adjusts its approach based on the specific cell configuration and DRC requirements, enabling it to handle complicated cells while maintaining efficiency.
3Manufacturing precision
If MILP based routing methods are used, then routing problems can be formulated as optimization problems, but they require DRCs to be expressed in conditional equality or inequality form making it difficult to support newer technology nodes
Solution Approach 1:
The patent changes the fundamental parameters of how routing is approached by moving from mathematical programming with explicit constraint formulation to reinforcement learning with implicit constraint satisfaction through learned policies. This parameter change allows the system to adapt to newer technology nodes and different DRC formulations without requiring reformulation of constraints, as the reinforcement learning model learns to handle various DRC styles through training data.
Solution Approach 2:
The patent creates a universal routing approach using reinforcement learning that can handle multiple technology nodes and DRC formulations through a single trained model. The reinforcement learning system learns general routing principles and DRC satisfaction strategies that are transferable across different technology nodes, eliminating the need for node-specific constraint formulations and improving versatility.
4Extent of automation
If reinforcement learning is applied to routing problems creating routing direction actions, then routing can be automated, but these approaches are limited to connecting routes without consideration of design rule violations
Solution Approach 1:
The patent merges routing and DRC checking into a unified reinforcement learning operation. Rather than treating routing direction selection and DRC compliance as separate steps, the reinforcement learning model simultaneously learns to perform both functions, selecting routing directions that inherently satisfy DRCs. This merging ensures that automated routing produces DRC-compliant results without requiring post-processing or separate validation steps.
Data Source
AI summary
An automatic standard cell layout generator that generates circuit layouts for an industry standard cell library on an advanced technology node leverages reinforcement learning (RL) to generate device placements in the layouts and also to fix design rule violations during routing. A genetic algorithm is utilized to generate routing candidates to which a reinforcement learning model is applied to correct the design rule constraint violations incrementally. A design rule checker provides feedback on the violations to the reinforcement learning model and the model learns how to fix the violations. A layout device placer based upon a simulated annealing method may also be utilized.