🔹 Problem Setup

• Goal: Predict SRAM cell Read SNM under process variation.
• Traditional approach: Monte Carlo → run 100k SPICE simulations → slow.
• AI-aided approach: Train ML model to learn the mapping from process parameters → SNM.

🔹 1. Collect Data

• Generate data using SPICE/TCAD simulations (smaller Monte Carlo sample, e.g., 5k runs).
• Features (inputs):
  • Vth of NMOS (ΔVtn)
  • Vth of PMOS (ΔVtp)
  • Channel length variation (ΔL)
  • Supply voltage (Vdd)
  • Temperature (T)
• Target (output):
  • Read SNM (in mV).

🔹 2. Example Dataset Format

🔹 3. Train ML Model (Python Example)

Here’s a simplified Python workflow using scikit-learn:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_absolute_error

# Load dataset (collected from SPICE/TCAD or silicon)
data = pd.read_csv("sram_variability.csv")

# Features and target
X = data[["DeltaVtn", "DeltaVtp", "DeltaL", "Vdd", "Temp"]]
y = data["SNM"]

# Split into train/test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train ML model (Gradient Boosting works well for tabular data)
model = GradientBoostingRegressor(n_estimators=300, max_depth=5)
model.fit(X_train, y_train)

# Evaluate
y_pred = model.predict(X_test)
mae = mean_absolute_error(y_test, y_pred)
print(f"Mean Absolute Error: {mae:.2f} mV")

# Example prediction
example = [[-20, 15, 1.0, 0.95, 25]]
print("Predicted SNM:", model.predict(example))

🔹 4. Deploy the AI Model

• Instead of running 100k SPICE Monte Carlo simulations, you run only 5k to train the ML model.
• Then use the ML model to predict SNM for millions of variation samples instantly.
• You can even estimate yield by counting the % of predictions below a SNM failure threshold.

🔹 5. Benefits

• Speed: Predictions in seconds instead of weeks of SPICE.
• Coverage: Explore much larger variation space.
• Accuracy: Within a few mV error compared to Monte Carlo SPICE (if trained properly).
• Scalability: Same workflow applies to delay, leakage, reliability, etc.

✅ Summary:
This is how AI replaces brute-force Monte Carlo in variability modeling. You simulate a smaller dataset, train an ML surrogate model, and then predict across the full variation space for fast yield estimation.