Santa Rosa, CA. Keysight Technologies today announced it has reached a new milestone in low-frequency noise measurements through its work with leading research centers in Europe, Middle East, Africa, and India (EMEAI). Using the new and WaferPro Express software, designers can now measure noise more accurately in an even broader range of electronic devices.
Noise is one of the main limiting factors in electronic devices, including sensitive parts such as sensors and memories. As a result, the ability to monitor noise using a statistical approach on a large number of samples and different devices is more critical than ever before.
“Since introducing the advanced low-frequency noise analyzer, Keysight has actively engaged with research centers that are world-renowned in the field of noise measurements,” said Cédric Pujol, EMEAI device modeling business development manager for Keysight EEsof EDA. “These collaborations have driven our developments into a broad range of applications, allowing the analyzer to increase its versatility in flicker-noise and random telegraph noise measurements. They also helped strengthen the use of the analyzer for devices used in industrial electronics and in 5G and IoT-based devices.”
The cornerstone of Keysight’s collaboration with research centers, such as Sweden’s Chalmers University, Germany’s Fraunhofer EMFT, Belgium’s imec, and France’s LAAS-CNRS, is the addition of WaferPro Express software, a measurement framework that benefits from Keysight’s years of experience in device modeling. The Keysight A-LFNA now provides designers the flexibility to transform their low-frequency noise measurements into mathematical models.
By controlling A-LFNA with WaferPro Express, we significantly improved our ability to set up sequences to characterize our extremely low-noise devices, while also gaining the flexibility to drive the whole measurement system effortlessly,” said Dipl.-Ing. (Univ.) Werner Muth, device development consultant to Fraunhofer EMFT in Munich, Germany. “The system can mathematically post-process our raw results in an automated way, allowing us to very quickly get insight into the noise figure improvement of our newly developed devices.”
“LAAS-CNRS has extensive and wide-ranging experience in the field of high- and low-frequency noise measurements, with two low-frequency experimental setups currently available, 400 mA max and 1 Hz to 1 MHz, as well as robust software for spectra extraction,” said Jean-Guy Tartarin, senior researcher at LAAS-CNRS. “With the new analyzer solution proposed by Keysight within the framework of the Low-Frequency Noise European Center, it will now be possible to address emerging challenges we face, like the number of samples and quiescent point under test. Using the automated solution, we’ll save both time and measurements.”
In addition, researchers at Chalmers University have also found success with the new A-LFNA solution. Using the analyzer, they were able to significantly improve the throughput of their high-quality, flicker-noise measurements. Also beneficial to researchers are the A-LFNA’s high-power capability and low noise floor, as well as its ability to flexibly switch between voltage and current amplifier operation—a capability that enables characterization of very different types of devices.
Other achievements resulting from the ongoing collaboration include an extension of the A-LFNA to enable noise measurements on CMOS sensors down to ultra-low frequencies (0.030 Hz) and on power devices with a 200-V bias voltage. In support of bulk silicon and silicon on insulator (SOI) technologies, the A-LFNA also now asserts a state of the art noise floor (2×10-27 A2/Hz). Additionally, it has been upgraded to measure noise power densities up to 40 MHz.
“I am pleased that Keysight’s 1 over f measurement system and more broadly Keysight’s device modeling portfolio meets the needs of leading research centers in Europe,” said Thierry Locquette, Keysight EDA’s EMEAI sales manager. “The combination of a state of the art solution with strong local support is a significant differentiator.”