The semiconductor test industry is at a critical juncture. Surging power density and complex test profiles are pushing the limits of existing temperature control solutions, and future development in the High-Power Compute, automotive, and 3D-IC segments is setting the stage for an increasingly challenging thermal environment. With temperature excursion events poised to escalate, Semiconductor test companies are searching for ways to reign in out-of-control devices. This paper delves into the evolution of active thermal control strategies by evaluating their progression, current state, and future implementation; where I will share one vision of what needs to be done and will invite others to share their thought to begin an exchange of ideas.
Traditional thermal solutions focused on removing heat and providing generalized consistent test temperatures as a second priority. This mindset resulted in less-than-ideal temperature control. More recent Active Thermal Control (ATC) systems focus on individual die-level or intra-die-level thermal control by adding die-focused sense and control elements. Dedicated ATC solutions have dramatically improved temperature stability during device testing but still leave much to desire. These Reactive-based ATC solutions help reduce the duration of the temperature excursion but do little to reduce the thermal spike that results from a near-instantaneous step change in power. Workarounds exist, but these workarounds negatively impact both test cost and yield.
One transformative approach to thermal control is machine learning, which can predict the changing thermal profiles of semiconductor devices and identify potential temperature excursions before they manifest. However, collecting, training, and implementing such a machine learning model is not trivial and will require extensive collaboration and changes to test equipment and the devices they test. For example, Automatic Test Equipment will need edge computing capability, robust data pipelines, communication protocols, and development around tester-delivered power. Probers and handlers will need more responsive active thermal control hardware and more advanced control schemes, and devices will need a variety of sensors to train the machine-learning model and provide real-time monitoring.
With the implementation of these solutions, excursion amplitudes will be cut in half, excursion time will be reduced by a staggering 80-90%, and the number of excursion events will plummet. With the thermal control bottleneck removed, the industry will be able to reduce the cost of tests and increase yield significantly, and everyone thinks that is cool!
