RL Timing Correction for IC Conductor Networks
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Solution Overview
Problem
Correcting timing failures in integrated circuits due to parasitic capacitances, resistance, and noise in conductor networks is a labor-intensive and time-consuming process that requires manual adjustments.
Innovation Solution
Utilizing a reinforcement learning agent with a neural network to automate the correction of timing failures by encoding conductor and repowering structures as a graph, receiving modification recommendations, and generating automated modification commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual adjustments are made to correct timing errors in conductor networks, then timing failures can be addressed, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system uses reinforcement learning agents that automatically learn and execute timing correction decisions without human intervention. The agent autonomously analyzes the conductor network, identifies timing failures, and applies appropriate corrections (such as adjusting conductor widths, adding buffers, or modifying routing) based on trained patterns from simulation data, enabling the system to self-correct timing issues.
Solution Approach 2:
The patent replaces manual mechanical adjustment processes with an automated computational system. Instead of human engineers manually analyzing and correcting timing errors, a reinforcement learning-based automated system performs the analysis and correction using trained models that have learned from extensive simulation data, substituting human cognitive and manual work with algorithmic processing.
2Reliability
If manual adjustments are made to correct timing errors in conductor networks, then timing failures can be addressed, but the process becomes labor-intensive
Solution Approach 1:
The reinforcement learning agent autonomously performs the entire timing correction workflow including analyzing conductor network characteristics, identifying timing failures, selecting appropriate correction strategies, and applying modifications. This eliminates the need for human operators to manually perform these complex tasks, making the process as easy as initiating the automated agent execution.
Solution Approach 2:
The patent substitutes manual engineering judgment and manual adjustment operations with an automated reinforcement learning system that has been trained on extensive simulation data. The system automatically performs tasks that would otherwise require skilled human operators, significantly reducing labor intensity and improving ease of operation.
3Reliability
If the conductor network is modified to correct timing failures, then signal timing can be improved, but parasitic capacitances and resistance may cause further degradation
Solution Approach 1:
The reinforcement learning agent is trained using simulation data that includes feedback about the effects of timing corrections on parasitic capacitances and resistance. The agent learns from this feedback to make corrections that improve timing while minimizing or compensating for parasitic effects. The simulated environment provides realistic feedback about how modifications affect signal integrity, allowing the agent to learn optimal correction strategies that account for parasitic factors.
Solution Approach 2:
The system performs preliminary analysis and planning before making actual corrections. The reinforcement learning agent evaluates multiple potential correction strategies and their expected effects on parasitic capacitances and resistance before selecting and applying the optimal correction. This preliminary evaluation prevents harmful parasitic effects from worsening the timing issue.
Data Source
AI summary
Disclosed herein is a computer implemented method of correcting a timing failure of a network of conductors and repowering structures in an integrated circuit design using a reinforcement learning agent. The reinforcement learning agent comprises a neural network. The method comprises: receiving a graph comprising nodes and edges that encodes said network of conductors and repowering structures; and receiving a modification recommendation from said reinforcement learning agent in response to inputting said graph into said reinforcement learning agent.