Driving Yield Improvement and Quality Control for Semiconductor Manufacturers

Techradar recent article about the semiconductor industry indicates that “The semiconductor industry is losing billions of dollars every year because of this obscure little quirk”. This post breaks down some of the reasons. 

The semiconductor industry is experiencing an unprecedented era of growth and innovation, with companies like TSMC recently joining the exclusive “$1 trillion club“. This remarkable achievement highlights the industry’s pivotal role as the backbone of modern technology, powering everything from smartphones and autonomous vehicles to the burgeoning fields of Artificial Intelligence (AI) and 5G networks. The relentless demand for smaller, faster, and more energy-efficient chips continues to accelerate, driven by these advancements and technology’s ever-increasing importance in our daily lives.

However, this rapid progression is not without its significant hurdles. One of the most critical challenges facing semiconductor manufacturers today is the persistent issue of high production yield loss. This problem is largely attributed to stochastic variability, a form of random patterning variation at the nanoscale, now considered the biggest obstacle to achieving high yields at the most advanced process nodes. Chris Mack, CTO of Fractilia, notes that this variability contributes to multibillion-dollar delays in bringing advanced process technology to high-volume manufacturing. Unlike conventional variability, stochastic effects cannot be eliminated with tighter controls; they demand probability-based design and measurement techniques for effective management. Legacy systems and traditional process control strategies often fall short, struggling to ingest, unify, and analyze the massive, petabyte-scale datasets generated by modern fabs from sensors, tools, and tests at every process step. This leads to CPU bottlenecks, high latency, and siloed data, impeding crucial yield analysis and root-cause investigations.

The success story of TSMC, founded in 1987 by Morris Chang as the world’s first dedicated semiconductor foundry, serves as an inspiration in overcoming such a challenge. By leveraging Advanced Analytics and AI for Yield Optimization: TSMC is “deeply invested in HPC” (High-Performance Computing) internally for manufacturing, including applying AI for “equipment health and yield prediction”. Their collaboration with NVIDIA on GPU-accelerated computational lithography (cuLitho) achieved a “50× speedup” in mask processing, which is critical for successful patterning and thus, yield at advanced nodes. This adoption of advanced tools suggests a proactive and successful approach to continuously optimizing and overcoming yield limitations.

Chang’s vision, supported by significant initial investment and technology transfer from the Taiwanese government and Philips, enabled TSMC to rapidly scale and become the undisputed leader in its field, even attracting integrated device manufacturers like Intel to outsource some of their production. His strategic leadership for over three decades cemented TSMC’s position as a foundational partner for virtually all major fabless semiconductor companies.

In this complex landscape, where the need for near real-time insights and advanced analytics is paramount, solutions like SQream’s GPU-accelerated analytics platform are proving transformative. SQream empowers semiconductor manufacturers to harness the power of their data, addressing the core issues that traditional systems struggle with.

How SQream Drives Yield Improvement and Quality Control:

SQream is optimal for the semiconductor industry’s need for multi-source data integration and massive data processing across numerous use cases, including yield optimization, predictive maintenance, and capacity planning. The platform leverages GPU acceleration, which offers a dramatic performance boost over CPU-based analytics due to its massive parallelism, allowing thousands of operations on data points simultaneously.

Consider the case of a leading Asian electronics manufacturer that was grappling with a complex and disparate data infrastructure, resulting in yields below 50%. Their daily data loading batches took over two days, rendering AI predictions irrelevant by the time they were produced, and their data infrastructure lacked a unified analytical platform for MES (Manufacturing Execution Systems) and MIS (Management Information Systems) data.

SQream provided a solution by:

• Consolidating Data Silos: SQream was implemented as their big data analytics platform, managing over 10PB of data and continuously feeding a custom-made AI platform. It replaced a legacy system, unifying previously disconnected data.
• Hyper-Speed Data Ingestion and Parsing: Daily, SQream handles up to 100TB of raw data generated by manufacturing equipment sensors and logic controllers, transforming it into analytics-ready data on the same day. A large South Korean chips supplier,  for example, gained the ability to ingest raw data directly into SQream and run complex parsing algorithms utilizing its blazing-fast GPU processors, a task that previously consumed vast CPU resources.
• Near-Real-Time Quality Analysis: In semiconductor manufacturing, chip quality is determined by thousands of test parameters across long, complicated, and demanding test processes to asure the chip’s quality. This generates massive floating-point datasets, requiring nonlinear multivariate series analysis to produce wafer quality matrices (X,Y coordinate data for each chip on a wafer). Traditional databases often struggle or even “halt the whole production cluster” when performing critical operations like pivoting and grouping on such large datasets to calculate yield ratios and identify faulty chips. SQream’s GPU-accelerated engine excels at these complex, multi-dimensional queries, enabling faster insight into quality assurance and yield ratios, and faster root cause analysis through GPU-accelerated data mining and AI.
• Transformative Business Results: This manufacturer achieved a 90% reduction in data collection and loading costs, an 8X faster report generation time, and a 99% reduction in AI analytics preparation time. Most notably, they experienced a dramatic yield increase from below 50% to 90%. As a senior director for IT strategy stated, “The ability to predict and take actions based on the current status of equipment is such a significant factor… it cannot be discussed in $$$ terms”.

By leveraging SQream’s advanced GPU-accelerated analytics, semiconductor manufacturers can unlock actionable insights from their data, moving beyond the constraints of legacy systems to achieve higher efficiency, accuracy, agility, and improved yield. This allows them to proactively manage operations, optimize resource allocation, and ultimately maintain a sustainable competitive advantage in an increasingly data-driven market.