AI Semiconductor Placement Automation

The patent replaces the mechanical manual placement process with an AI-based automated system. The neural network model learns from training data consisting of semiconductor design patterns and rules, automatically generating placement sequences without manual intervention. This substitution maintains design flexibility through learnable patterns while dramatically improving productivity by eliminating the bottleneck of manual engineering work.

The system enables self-service automation where the AI model independently performs the placement task. The neural network processes design inputs, generates placement sequences, and outputs results without requiring continuous human guidance. The model serves itself by learning from historical data and applying learned patterns to new designs, reducing reliance on individual engineer expertise while maintaining consistent quality.

The patent transforms the placement problem into a parameter optimization task for the neural network. By adjusting training parameters, learning rates, and model architecture parameters, the system learns to consistently produce high-quality placements. The AI model internalizes design rules as learned parameters, enabling consistent application of best practices across different designs while maintaining adaptability to new requirements through retraining or fine-tuning.

The system implements feedback mechanisms where the neural network learns from training data that includes successful placement patterns and design rules. The model receives feedback during training through loss functions that measure placement quality, continuously improving its ability to produce consistent results. This feedback loop enables the system to maintain high design consistency while adapting to different design scenarios.

The patent segments the complex design automation task into manageable components: data preprocessing, neural network model training, placement sequence generation, and result output. The neural network itself is structured with separate layers (embedding layer, transformer encoder, decoder) that handle different aspects of the placement problem. This segmentation makes the complex AI system more manageable and easier to implement while maintaining high productivity benefits.

The patent introduces intermediate representations such as embeddings that transform raw design data into a format suitable for neural network processing. The transformer model acts as an intermediary that bridges the gap between traditional EDA tools and AI-based optimization. These intermediaries simplify the integration of AI complexity into existing workflows, making the system easier to implement while preserving productivity gains.

The patent performs preliminary action by pre-training the neural network model on extensive training data before actual placement tasks. This offline training phase, while computationally intensive, is done once and then the trained model can be reused for multiple placement tasks. The pre-computed knowledge in the trained model enables fast inference during actual design work, reducing design time while amortizing the computational energy cost over multiple uses.

The system uses partial action by generating placement sequences step-by-step rather than computing all possible placements simultaneously. The neural network generates placements in a sequence, making decisions incrementally based on current state. This approach reduces the computational burden compared to exhaustive methods while still achieving optimal or near-optimal results, balancing design time reduction with reasonable energy consumption.