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Average price per chip affects profitability.
513 technical terms and definitions
Average price per chip affects profitability.
Average Selling Price represents typical per-unit revenue across product mix.
Ratio of feature depth to width affects etch difficulty.
High aspect ratio trenches and vias require advanced deposition techniques ensuring complete bottom coverage without void formation.
Sentiment toward specific aspects.
Identify sentiment toward specific aspects.
Percentage surviving packaging.
Generate test assertions.
Identifiable reason for process variation.
Model's response.
Assistant message is model output. Follows user input. May include reasoning, code, structured data.
Different penalties for different errors.
Asymmetric loss functions penalize deviations differently above and below target.
Asynchronous generation handles multiple requests concurrently maximizing throughput.
Async/await enables concurrent I/O without threads. Event loop. Python asyncio, JavaScript promises.
Save state without blocking training.
Clock-free circuit design.
Non-blocking GPU operations.
Allow asynchronous updates.
At-speed testing operates devices at full rated frequency detecting timing failures missed by slower testing.
Test at operational frequency.
High-speed testers for production testing.
Automated Test Equipment executes test programs coordinating instruments timing and data collection for high-throughput semiconductor testing.
Retrieval-augmented model for knowledge-intensive tasks.
Robot that operates in normal atmosphere or N2 environment.
3D atomic-scale composition analysis.
Local atomic neighborhood features.
Measure roughness at atomic scale.
Automatic Test Pattern Generation algorithmically creates test vectors to detect specific faults or maximize fault coverage in digital circuits.
Attention mechanism as retrieval.
Add positional bias efficiently.
Study how far attention reaches.
Trace attention patterns through network.
Attention flow tracks information propagation through attention mechanisms across layers.
Attention mechanisms in time series weight historical values adaptively for improved forecasting accuracy.
Different functions of attention heads.
Scale attention by head dimension.
Binary mask indicating which tokens to attend to vs ignore (for padding).
Attention-based graph pooling learns soft cluster assignments through self-attention over node features.
Aggregate attention across layers.
Aggregate attention across layers for interpretability.
Attention rollout computes effective attention by multiplying attention weights across transformer layers.
Keep first tokens for stable attention.
Transfer attention maps from teacher.
See what model focuses on.
Visualize what model attends to.
Attention visualization displays learned attention weights revealing which inputs influence outputs.
Inspect attention weights to see what the model focuses on.
Attention-based explanations visualize which user history items or features influenced specific recommendations.
Use attention to weight modalities.