The Data Dilemma In Semiconductor Testing And Why It Matters: Part 1

In today’s semiconductor industry, machine learning (ML) is no longer a buzzword — it’s an operational necessity. From optimizing test flows to identifying device drifts and executing advanced analytics like VMIN or trimming, ML-based applications are increasingly used to boost yields, improve quality, and lower test costs. But there’s a catch.

To make these intelligent applications work, engineers must access and share device test data across multiple test insertions, often spanning different facilities and partners — think fabs, OSATs (Outsourced Semiconductor Assembly and Test providers), and product owners themselves. This concept, known as Data Feed Forward (DFF), is key to real-time test optimization. But implementing DFF in practice? That’s where the real challenge begins.

The reality of data transfer in semiconductor manufacturing

Transferring device test data between enterprises isn’t as simple as sending a file. Transferring data between different companies is never easy. These manufacturing environments are highly security-conscious and require a secure platform to direct the data-on-demand to target recipients.

Then there’s the data itself. Data Preparation is a big effort in data analytics process. Before test data can be used in downstream insertions, it must be organized, filtered, and formatted in a way that the next test program can understand. For companies trying to build internal DFF infrastructure, this means a heavy investment in both people and tools.

In many cases, engineering teams are forced to spend more time wrangling data than running tests. That’s a huge, missed opportunity.

Why DFF matters more than ever

Semiconductor test floors are now expected to function more like real-time analytics engines. Each insertion can generate insights that improve the next—if the data is available when and where it’s needed. Without robust DFF, you lose the ability to:

• Adjust tests condition or test limits “in situ” based on binning/performance history across sites/test operations
• Improve trimming and search-oriented test efficiency
• Maximize Final Test yield and optimize test operations to reduce Cost of Test

DFF data coming from different insertions can be used within the test program but also outside of the test program. In some applications, customers may want to run in parallel with the test program some computationally intensive AI/ML applications. In this case, they can implement DFF running AI/ML inferences, using dedicated edge servers in 1:1 configuration (1 server per tester) or 1:N configuration (1 server for N testers).

Clearly, DFF isn’t just a technical feature. It’s a critical enabler for smarter, faster semiconductor testing.

So how can organizations embrace DFF without getting stuck in complex data mediations and IT overhead? That’s where Advantest Cloud Solutions (ACS) Data Feed Forward comes in.

Stay tuned for Part 2 of this blog, where we dive into how ACS DFF streamlines data transfer, eliminates operational roadblocks, and enables high-impact machine learning across test insertions—without burdening your engineering team.