Process Data Analytics in Advanced Manufacturing

# Process Data Analytics in Advanced Manufacturing

## Introduction & Motivation

Process Data Analytics systematically aggregates, transforms, and analyzes high-dimensional manufacturing telemetry streams to isolate process drift, identify multivariate anomalies, and optimize operational throughput. In modern semiconductor fabs producing thousands of wafers daily, each tool generates gigabytes of time-series sensor data across hundreds of process variables.

Motivation: Transform raw multi-sensor telemetry into actionable diagnostics and predictive controls for yield enhancement.

Applications: Chamber-to-chamber matching, etch rate drift detection, Virtual Metrology (VM), Statistical Process Control (SPC).

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## Core Concepts & Theoretical Foundations

### Principal Component Analysis (PCA) for MSPC

Multivariate Statistical Process Control (MSPC) relies on PCA to reduce raw sensor matrix

to score space

(

):

Fault detection is conducted using Hotelling's

statistic and Squared Prediction Error (

/

-statistic):

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## Python Laboratories & Practical Implementations

### Lab 1: Multi-Sensor Time-Series Data Generator

import numpy as np

def generate_process_telemetry(n_steps=200, n_sensors=5):
 np.random.seed(42)
 t = np.linspace(0, 50, n_steps)
 sensors = np.zeros((n_steps, n_sensors))
 for i in range(n_sensors):
 freq = 0.1 * (i + 1)
 sensors[:, i] = np.sin(freq * t) + 0.1 * np.random.normal(size=n_steps)
 sensors[150:, 2] += 2.5 # Inject artificial sensor step shift
 return t, sensors

t, data = generate_process_telemetry()
print("Telemetry Matrix Shape:", data.shape)
print("Normal region mean (Sensor 2):", round(np.mean(data[:100, 2]), 3))
print("Shifted region mean (Sensor 2):", round(np.mean(data[150:, 2]), 3))

### Lab 2: Hotelling T² and Q-Statistic Calculation

import numpy as np

def compute_mspc_statistics(X, k_components=2):
 mean = np.mean(X, axis=0)
 X_centered = X - mean
 U, S, Vt = np.linalg.svd(X_centered, full_matrices=False)
 P = Vt[:k_components, :].T
 scores = X_centered @ P
 recon = scores @ P.T
 errors = X_centered - recon
 Q = np.sum(errors ** 2, axis=1)
 cov_scores = np.cov(scores, rowvar=False)
 inv_cov = np.linalg.inv(cov_scores)
 T2 = np.sum((scores @ inv_cov) * scores, axis=1)
 return T2, Q

t, data = generate_process_telemetry()
T2, Q = compute_mspc_statistics(data, k_components=2)
print("Normal mean T2:", round(np.mean(T2[:100]), 3))
print("Fault mean T2:", round(np.mean(T2[150:]), 3))
print("Normal mean Q:", round(np.mean(Q[:100]), 3))
print("Fault mean Q:", round(np.mean(Q[150:]), 3))

### Lab 3: Rolling Window Feature Extraction Engine

import numpy as np

def extract_rolling_features(signal, window_size=10):
 n = len(signal)
 means = []
 stds = []
 slopes = []
 for i in range(window_size, n):
 win = signal[i-window_size:i]
 means.append(np.mean(win))
 stds.append(np.std(win))
 x = np.arange(window_size)
 slope = np.polyfit(x, win, 1)[0]
 slopes.append(slope)
 return np.array(means), np.array(stds), np.array(slopes)

t, data = generate_process_telemetry()
means, stds, slopes = extract_rolling_features(data[:, 0], window_size=15)
print("Extracted feature windows:", len(means))
print("Average rolling std:", round(np.mean(stds), 4))

### Lab 4: Western Electric SPC Rule Checker

import numpy as np

def check_spc_rules(data, mean, std):
 ucl = mean + 3 * std
 lcl = mean - 3 * std
 violations = []
 for idx, val in enumerate(data):
 if val > ucl or val < lcl:
 violations.append((idx, val, "Rule 1: Beyond 3-Sigma"))
 return violations

sensor_data = np.random.normal(10.0, 1.0, 100)
sensor_data[45] = 14.2 # Out of control point
sensor_data[80] = 5.1 # Out of control point
violations = check_spc_rules(sensor_data, mean=10.0, std=1.0)
print(f"Detected {len(violations)} SPC violations:")
for v in violations:
 print(f" Index {v[0]}: Value = {v[1]:.2f} | {v[2]}")

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## Summary & Best Known Methods

1. Dimension Reduction: Standardize multivariate time series before applying PCA score transformations.
2. Dual Metric Monitoring: Utilize

for systematic in-model drift and

-statistic for out-of-model noise/faults.
3. Automated Feature Pipelines: Feature engineering across sliding windows enables early anomaly flagging.