┌─────────────────────────────────────────────────┐
│                 User Interface                   │
├─────────────────────────────────────────────────┤
│ Chat Interface │ Dashboards │ Mobile │ API      │
├─────────────────────────────────────────────────┤
│                RAG Engine Core                   │
├─────────────────────────────────────────────────┤
│ Query Router │ Context Retrieval │ LLM Service  │
├─────────────────────────────────────────────────┤
│              Knowledge Base Layer                │
├─────────────────────────────────────────────────┤
│ Vector Store │ Graph DB │ Document Store        │
├─────────────────────────────────────────────────┤
│                Data Integration                  │
├─────────────────────────────────────────────────┤
│ MES │ APC │ FDC │ WAT │ CP │ Defect │ Tool Health│
└─────────────────────────────────────────────────┘

# SOP Implementation Steps
1. Document Ingestion
   - PDF/Word extraction using PyPDF2/python-docx
   - Confluence/SharePoint API integration
   - Metadata extraction (author, date, process area)

2. Text Preprocessing
   - Remove formatting artifacts
   - Standardize terminology using semiconductor glossary
   - Split into semantic chunks (512-1024 tokens)

3. Vector Generation
   - Use sentence-transformers/all-MiniLM-L6-v2
   - Generate embeddings for each chunk
   - Store with metadata (document_id, chunk_id, process_area)

# Production Implementation
1. Install Dependencies
   pip install faiss-gpu transformers sentence-transformers

2. Build Vector Index
   - Load pre-processed documents
   - Generate embeddings batch-wise (1000 docs/batch)
   - Create FAISS index with IVF clustering for fast retrieval
   - Save index to persistent storage

3. Retrieval Function
   - Query embedding generation
   - Top-k similarity search (k=5-10)
   - Context ranking and filtering
   - Metadata-based result refinement

User Query → Embedding → FAISS Search → Context Retrieval → 
LLM Augmentation → Response Generation → Quality Check → Output

# DAG Configuration
dag = DAG('semiconductor_etl_pipeline',
    schedule_interval='@daily',
    start_date=datetime(2024, 1, 1),
    catchup=False
)

# Task Dependencies
extract_mes >> transform_lot_data >> load_vector_store
extract_apc >> transform_process_data >> load_vector_store  
extract_fdc >> transform_fault_data >> load_vector_store

# Metadata Tagging Strategy
metadata_template = {
    'lot_id': 'LOT_12345',
    'wafer_id': 'W001',
    'process_step': 'Lithography',
    'timestamp': '2024-01-15T10:30:00Z',
    'tool_id': 'TOOL_A1',
    'data_source': 'MES',
    'quality_flag': 'validated'
}

# Data Curation Process
1. Internal Data Collection
   - Anonymized yield logs → Q&A pairs
   - Equipment troubleshooting → Problem-solution pairs
   - Process optimization → Parameter-outcome pairs

2. External Data Integration  
   - arXiv papers on semiconductor ML
   - SEMI standards documentation
   - Open-source fab simulation data

3. Data Quality Assurance
   - Remove PII and proprietary details
   - Balance defect type distributions
   - Validate technical accuracy with domain experts

# Production Environment
pip install transformers==4.35.0 peft==0.6.0 datasets==2.14.0
pip install torch==2.1.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install accelerate==0.24.0 bitsandbytes==0.41.0

# Optimal LoRA Settings for Semiconductor Domain
lora_config = LoraConfig(
    r=16,                    # Rank (balance between efficiency and performance)
    lora_alpha=32,           # Scaling factor
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
    lora_dropout=0.1,        # Prevent overfitting
    bias="none",             # No bias adaptation
    task_type="CAUSAL_LM"    # Language modeling task
)

# Complete Training Workflow
def train_semiconductor_model():
    # 1. Load base model
    model = AutoModelForCausalLM.from_pretrained(
        "meta-llama/Llama-2-7b-hf",
        torch_dtype=torch.float16,
        device_map="auto"
    )

    # 2. Apply LoRA adaptation
    model = get_peft_model(model, lora_config)

    # 3. Prepare dataset
    dataset = load_semiconductor_dataset()  # Custom function
    tokenized_dataset = dataset.map(tokenize_function)

    # 4. Training configuration
    training_args = TrainingArguments(
        output_dir="./semiconductor-llama-lora",
        num_train_epochs=3,
        per_device_train_batch_size=4,
        gradient_accumulation_steps=4,
        warmup_steps=100,
        learning_rate=2e-4,
        fp16=True,
        logging_steps=10,
        save_steps=500,
        evaluation_strategy="steps",
        eval_steps=500
    )

    # 5. Execute training
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_dataset["train"],
        eval_dataset=tokenized_dataset["validation"]
    )

    trainer.train()
    return model

# FastAPI Implementation
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import asyncio

app = FastAPI(title="Semiconductor AI Assistant")

class QueryRequest(BaseModel):
    query: str
    context_type: str = "general"  # Options: sop, bkm, defect_analysis
    lot_id: Optional[str] = None

class QueryResponse(BaseModel):
    response: str
    confidence: float
    sources: List[str]
    processing_time: float

@app.post("/query", response_model=QueryResponse)
async def process_query(request: QueryRequest):
    start_time = time.time()

    # 1. Context retrieval via RAG
    context = await retrieve_context(
        query=request.query,
        context_type=request.context_type,
        lot_id=request.lot_id
    )

    # 2. LLM generation
    response = await generate_response(
        query=request.query,
        context=context
    )

    # 3. Quality validation
    confidence = calculate_confidence(response, context)

    processing_time = time.time() - start_time

    return QueryResponse(
        response=response,
        confidence=confidence,
        sources=extract_sources(context),
        processing_time=processing_time
    )

# Additional endpoints
@app.post("/defect-analysis")
async def analyze_defects(fdc_logs: dict):
    """Analyze FDC logs for defect classification"""

@app.post("/yield-prediction")  
async def predict_yield(process_params: dict):
    """Predict yield based on process parameters"""

@app.get("/lot-trace/{lot_id}")
async def trace_lot(lot_id: str):
    """Complete lot traceability report"""

# Multi-dimensional Analysis Engine
class MultiViewAnalyzer:
    def __init__(self, llm_model, data_connectors):
        self.model = llm_model
        self.connectors = data_connectors

    async def generate_analysis_report(self, query: str, dimensions: List[str]):
        """
        Generate multi-dimensional analysis reports

        Args:
            query: Analysis request (e.g., "yield impact analysis")
            dimensions: Analysis dimensions ["tool_health", "process_step", "time"]
        """

        # 1. Data aggregation across dimensions
        aggregated_data = {}
        for dimension in dimensions:
            connector = self.connectors[dimension]
            aggregated_data[dimension] = await connector.fetch_data(query)

        # 2. LLM-powered analysis
        prompt = self.build_analysis_prompt(query, aggregated_data, dimensions)
        analysis = await self.model.generate(prompt)

        # 3. Visualization recommendations
        viz_suggestions = self.suggest_visualizations(dimensions, aggregated_data)

        return {
            "analysis": analysis,
            "data": aggregated_data,
            "visualizations": viz_suggestions,
            "dimensions": dimensions
        }

# Example Usage
analyzer = MultiViewAnalyzer(llm_model, data_connectors)
report = await analyzer.generate_analysis_report(
    query="CP test yield impact analysis",
    dimensions=["tool_health", "wafer_position", "process_parameters"]
)

# RLHF Implementation Framework
class SemiconductorRLHF:
    def __init__(self, base_model, reward_model):
        self.base_model = base_model
        self.reward_model = reward_model

    def collect_preferences(self, queries: List[str], responses: List[List[str]]):
        """
        Collect engineer preferences for response ranking

        Args:
            queries: List of technical queries
            responses: List of response pairs for each query
        """
        preferences = []
        for i, (query, response_pair) in enumerate(zip(queries, responses)):
            # Present to domain experts for ranking
            expert_rating = self.get_expert_feedback(query, response_pair)
            preferences.append({
                "query": query,
                "responses": response_pair,
                "preference": expert_rating,
                "expert_id": expert_rating["expert_id"],
                "confidence": expert_rating["confidence"]
            })
        return preferences

    def train_reward_model(self, preferences):
        """Train reward model based on expert preferences"""
        # Implementation using transformers and preference learning
        pass

    def ppo_fine_tuning(self, queries: List[str]):
        """PPO-based fine-tuning using reward model"""
        # Implementation using TRL library
        pass

# Training Pipeline
rlhf_trainer = SemiconductorRLHF(llama_model, reward_model)

# Step 1: Collect preferences from process engineers
preferences = rlhf_trainer.collect_preferences(
    queries=["Analyze CP data for yield impact", "Trace wafer processing issues"],
    responses=model_generated_responses
)

# Step 2: Train reward model
rlhf_trainer.train_reward_model(preferences)

# Step 3: Fine-tune with PPO
aligned_model = rlhf_trainer.ppo_fine_tuning(evaluation_queries)

# Semiconductor-Specific Evaluation Metrics
class SemiconductorEvaluator:
    def __init__(self):
        self.metrics = {
            "technical_accuracy": self.evaluate_technical_accuracy,
            "terminology_precision": self.evaluate_terminology,
            "traceability_completeness": self.evaluate_traceability,
            "actionability": self.evaluate_actionability
        }

    def evaluate_model_performance(self, model, test_queries):
        results = {}
        for metric_name, evaluator in self.metrics.items():
            score = evaluator(model, test_queries)
            results[metric_name] = score
        return results

    def evaluate_technical_accuracy(self, model, queries):
        """Validate technical correctness with domain experts"""
        correct_responses = 0
        for query in queries:
            response = model.generate(query)
            expert_validation = self.get_expert_validation(query, response)
            if expert_validation["correct"]:
                correct_responses += 1
        return correct_responses / len(queries)

# Benchmark Test Cases
benchmark_queries = [
    "What are the root causes for high Vt variation in PMOS devices?",
    "Analyze FDC alarms for lithography step in lot ABC123",
    "Recommend process adjustments for improving CP yield",
    "Trace defect source from WAT to final test results"
]

evaluator = SemiconductorEvaluator()
performance_scores = evaluator.evaluate_model_performance(
    model=fine_tuned_model,
    test_queries=benchmark_queries
)