Reinforcement Learning for Semiconductor Element Position Optimization
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Solution Overview
Problem
The manual design of semiconductor elements in semiconductor manufacturing is inefficient, leading to increased working hours, manpower requirements, and inconsistent results due to operator variability, as well as low working efficiencies and inconsistent mass production quality.
Innovation Solution
A reinforcement learning apparatus and method that constructs a learning environment based on user-specific semiconductor design data, using a simulation engine and reinforcement learning agent to optimize semiconductor element positions through simulation, providing reward information based on connection and positional data to determine optimal placements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual design is used to dispose semiconductor elements, then operator flexibility and adaptability are maintained, but working hours and manpower requirements increase significantly
Solution Approach 1:
The system enables self-service automation where the reinforcement learning agent autonomously determines optimal element positions without continuous human intervention. The agent learns from simulated designs and automatically optimizes layouts, reducing reliance on manual operator input while maintaining design quality.
Solution Approach 2:
The patent replaces manual mechanical design processes with an automated reinforcement learning system. The mechanical interaction between operators and design tools is substituted by an AI agent that interacts with a simulated environment, automatically generating and optimizing semiconductor element dispositions.
2Adaptability or versatility
If manual design is used to dispose semiconductor elements, then operator know-how can be applied, but results of mass production become inconsistent
Solution Approach 1:
The reinforcement learning system implements continuous feedback loops where the agent receives reward signals based on design quality metrics. This feedback mechanism allows the system to learn from past designs and consistently apply optimal strategies across mass production, eliminating variability while preserving effective design patterns.
Solution Approach 2:
The system transforms subjective operator know-how into objective, quantifiable parameters that can be consistently applied. By converting design expertise into learnable parameters and reward functions, the system maintains the essence of operator knowledge while ensuring consistent application across all production units.
3Productivity
If reinforcement learning is used to optimize element positions, then working efficiency is improved, but construction of learning environment requires computational resources
Solution Approach 1:
The patent creates simplified copies or simulations of the actual semiconductor design environment. Instead of requiring complex real-world infrastructure, the system uses simulated environments that replicate essential design constraints and objectives, reducing computational overhead while maintaining training effectiveness.
4Productivity
If automated reinforcement learning is used, then manpower requirements are reduced, but design accuracy must be maintained
Solution Approach 1:
The patent replaces manual design mechanics with automated reinforcement learning mechanics. The AI agent learns optimal design strategies through simulation and automatically applies them, maintaining high precision while eliminating manual labor. The system substitutes human cognitive processes with algorithmic decision-making that can consistently achieve high accuracy.
Data Source
AI summary
Disclosed are an apparatus and a method for reinforcement learning for semiconductor element position optimization based on semiconductor design data. According to the present disclosure, a learning environment may be constructed based on a user's semiconductor design data such that optimal positions of semiconductor elements are provided during a semiconductor design process through reinforcement learning using simulation.