How to Scale Parallel Device Testing with Python and FPGA-Based Tools

As electronic devices continue to increase in complexity, traditional serial test approaches can limit throughput and extend development and production timelines. Parallel device testing provides a practical way to reduce overall test time, but scaling these systems requires careful consideration of automation, synchronization, and measurement coordination.

In this webinar, we present practical approaches for scaling electronic device testing through parallel test automation using FPGA-based, reconfigurable instrumentation. We will examine how software-defined test systems allow instruments such as oscilloscopes, spectrum analyzers, and frequency response analyzers to be dynamically configured and coordinated within a unified test framework. The session will cover how parallel test setups support both time-domain and frequency-domain measurements up to 2 GHz, enabling multiple devices or channels to be tested simultaneously while maintaining measurement accuracy and timing alignment. We will run these tests on actual evaluation modules during a live demonstration.

Attendees will also learn how Python-based automation simplifies test setup, execution, and reuse across different test scenarios. By moving from serial to parallel measurements, engineering teams can achieve substantial reductions in total test time, improve lab utilization, and accelerate device characterization, validation, and production test workflows.

By attending, participants will:

• Understand the fundamentals of parallel device testing and its impact on test throughput


• Learn how FPGA-based, reconfigurable Moku instruments enable flexible parallel test architectures


• See how oscilloscopes, spectrum analyzers, and frequency response analyzers can be coordinated for time- and frequency-domain measurements


• Understand how Python-based automation enables scalable and repeatable test workflows


• Identify where parallel test automation delivers meaningful time savings in R&D and production environments