Reinforcement Learning Circuit Design for Process Variation
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Solution Overview
Problem
Integrated circuits manufactured by semiconductor processes face variations that can lead to reliability and yield issues, requiring robust designs that conform to specifications despite these variations.
Innovation Solution
A method and system for designing integrated circuits using reinforcement learning, which involves generating output data through simulation, determining a reward variable by estimating circuit variations, obtaining an action variable from a reinforcement learning agent, training the agent, and updating the state variable based on the action variable to optimize circuit design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional circuit design methods are used, then design speed may be maintained, but the circuit cannot adequately account for process variations leading to reduced reliability and yield
Solution Approach 1:
The patent implements feedback by using reinforcement learning where the agent receives reward signals based on circuit performance metrics (yield, reliability) and adjusts design parameters iteratively. The simulation results feed back into the reward calculation, which guides subsequent design decisions, creating a closed-loop optimization system that accounts for process variations.
Solution Approach 2:
The patent applies parameter changes by optimizing circuit design parameters (such as transistor dimensions, material properties, or architectural choices) through reinforcement learning. The agent explores different parameter combinations to find designs that maintain performance across process variations, thereby improving reliability without manually increasing design complexity.
2Productivity
If robust circuit design accounting for process variations is implemented, then reliability and yield improve, but design time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by performing comprehensive process variation analysis and robustness optimization during the early design stages using reinforcement learning. Instead of iterating through multiple design revisions later, the agent learns to account for variations upfront, reducing total design time despite the computational intensity of the initial optimization phase.
Solution Approach 2:
The patent substitutes traditional manual or rule-based design iteration with reinforcement learning-based automated optimization. This replaces the mechanical process of repeated simulation-and-adjust cycles with an intelligent agent that learns optimal designs more efficiently, reducing overall design time despite the complexity of the learning process.
3Manufacturing precision
If reinforcement learning is used to optimize circuit design, then robustness against process variations improves, but computational complexity and training requirements increase
Solution Approach 1:
The patent uses an intermediary approach by introducing a reinforcement learning agent as a mediator between the circuit design space and the optimization goal. The agent translates design requirements into optimized parameters while accounting for process variations, acting as an intelligent intermediary that manages the complexity of finding robust designs without requiring direct manual intervention in the complex optimization process.
Data Source
AI summary
Disclosed is a method and system for designing a circuit. The method of designing a circuit based on reinforcement learning may include obtaining a state variable of the reinforcement learning, obtaining output data by performing a simulation based on the state variable, calculating a reward variable of the reinforcement learning based on the output data, obtaining an action variable from an agent based on the state variable and the reward variable, training the agent based on the state variable, the reward variable, and the action variable, and updating the state variable based on the action variable, wherein the calculating of the reward variable includes estimating a variation of the circuit based on the state variable, and calculating the reward variable based on the estimated variation.