Whether you’re supplying microcontrollers for ADAS systems, power semiconductors for EVs, precision analog circuits for medical implants, radiation-hardened processors for satellites, or mission-critical components for defense applications, this webinar will show you the power of PAT++ — an intelligence-driven data analytics solution that has helped companies cut their yield loss while significantly improving quality and reducing field failure risks
Date: August 20, 2025
Time: 10:00 AM PST/ 12:00 PM CST
Date: August 27, 2025
Time: 3 PM CET (Central European Time) / 2PM UK Time
The semiconductor market across automotive, medical devices, aerospace, and defense represents a $200+ billion opportunity, but only for those who can meet the increasingly stringent quality and reliability demands. With chip complexity increasing and field failure risks becoming more costly, traditional Gaussian-based outlier detection methods are broad brushing statistical data and missing out on critical signatures that could potentially discard perfectly good die while allowing defective parts to escape. Industry leaders need smarter, data-driven solutions that adapt to the true statistical behavior of their test data.
Whether you’re supplying microcontrollers for ADAS systems, power semiconductors for EVs, precision analog circuits for medical implants, radiation-hardened processors for satellites, or mission-critical components for defense applications, this webinar will show you the power of PAT++ — an intelligence-driven data analytics solution that has helped companies cut their yield loss while significantly improving quality and reducing field failure risks.
Why traditional PAT assumptions are costing you good chips and letting defects escape — and real-world examples that prove it, including costly field failures of otherwise known good die
6 advanced analytics methods that go beyond basic PAT, including Traditional PAT, MVP, MZPAT, GDBN, GDBN-Z/ZPAT, NNR
The Key Is in the Signature: Why identifying signature patterns in your data is critical for accurate test analytics and preventing field failures
How PAT++ automatically detects and adapts to upstream process shifts — and corrects course in real time to maintain both yield and quality
A real customer case study: How good die were being lost due to broad-brushing statistical assumptions while defective parts escaped detection — and how PAT++ identified and resolved both issues
See how it intelligently detects normal, log-normal, chi-square, uniform, and multi-modal distributions and practical implementation strategies for integrating PAT++ with your existing test flow while maintaining engineering control and flexibility.
Aftkhar Aslam is a distinguished executive and strategist with more than 30 years of experience in the semiconductor industry. He has consistently demonstrated expertise in bridging engineering excellence with digital transformation throughout the semiconductor ecosystem. At yieldWerx, Aftkhar leads efforts to advance Semiconductor Data & Yield Analysis across the entire product lifecycle, enabling global fabs, IDMs, and fabless companies to extract actionable insights in design, manufacturing, and packaging using AI-driven analytics and closed-loop traceability. His career encompasses significant engineering and leadership roles at Texas Instruments and Accenture, where he managed enterprise-wide digital transformation initiatives, such as the development and implementation of Digital Twins and IX Solutions, for prominent organizations including Intel, Qualcomm, Lam Research, GlobalFoundries, STMicroelectronics, and Microsoft.
CEO and Co-Founder
of yieldWerx Semiconductor.
