TMR sensors are about to get very high senstivity—with help from AI, custom chips, and smart materials. But can they deliver in the real world?
Tunnel Magnetoresistance (TMR) sensing is no longer just about the sensor itself—it’s evolving into a complete end-to-end solution. A key development on this path is the potential to achieve up to a 1,000x performance gain. This improvement will come from the combined effect of three elements: enhanced TMR sensor stacks, integration with custom ASICs, and the use of artificial intelligence to reduce noise in measurements. Together, these advancements aim to transform how data from TMR sensors is captured, processed, and applied. In a recent conversation with Kia Nazarpour, CSO at Neuranics, Nidhi Agarwal from Electronics For You (EFY) learned about a new approach to TMR sensing as a complete solution, aiming for a very high performance gain.
When it comes to real-world interference—such as EMI, temperature changes, mechanical springs, or vibration—the sensors have been tested and perform well. “We have been actively publishing results, including open-access data on sensor performance under vibration and perturbation,” Kia added.
The sensors have already been tested under real-world interference—such as electromagnetic interference (EMI), temperature changes, mechanical springs, and vibration—and have performed well. “We have been actively publishing results, including open-access data on sensor performance under vibration and perturbation,” Kia says. These results show that even with standard vibration removal techniques, the latest generation of TMR sensors works reliably.
Demonstrations are a major part of their outreach. At each trade show, visitors can try out the sensors for gesture recognition by wearing them on their arms. “A recent example was at the Sensors Converge conference, where the gesture recognition demo using TMR sensors was well received,” Kia shared.
Beyond gesture recognition and wearables, TMR sensors are now being applied in a range of new fields, including quantum computing, agriculture, and robotics. These opportunities are being enabled by increasing the sensitivity of the sensors—now reaching 1 picoTesla at 1 Hertz noise performance.
To boost sensitivity further, the team has explored the use of exotic materials, achieving TMR ratios up to 600% in laboratory settings. However, Kia pointed out that these lab-based materials often do not meet the strict requirements of semiconductor manufacturing. “The risk of material contamination in standard fabrication facilities is high, and process targets are tightly constrained,” he explained.
For this reason, Neuranics is taking a grounded approach: instead of relying on exotic materials, they are working within existing material systems and fine-tuning parameters to reach performance goals. “By combining insights from low-TRL [technology readiness level] physics with practical semiconductor fabrication constraints, we aim to make the 1,000x gain,” said Kia.
The full article will be live soon — stay tuned.
