Abstract
Memtransistors, three-terminal devices that combine the functionalities of memristors and transistors, offer a promising route for analog computing through their non-volatile behavior, low power consumption, and gate-tunable control. These features make them particularly well-suited for sensor fusion in autonomous systems. However, such tasks are typically implemented using digital Kalman filters, which suffer from high power consumption and limited real-time adaptability due to analog-to-digital conversion and iterative computation. Existing analog approaches based on memristors also fall short in handling multi-dimensional data under complex driving scenarios. To overcome these challenges, an analog multi-stage Kalman filtering system integrated with MoS2 memtransistors is presented, designed for multi-dimensional sensor data in autonomous driving. The three-terminal memtransistor enables multi-level conductance (1024) and excellent electrostatic control. This ensures a wide modulation range (>103) and exceptional linearity (R2 = 0.997) for Kalman gain (K), facilitating robust adaptation to complex driving conditions. The proposed system effectively handles sensor obstructions while achieving a 13-fold reduction in power consumption and a 59-fold decrease in latency compared to conventional digital circuits. These results demonstrate the potential of memtransistor-based analog computing for real-time, energy-efficient sensor fusion in next-generation autonomous systems.
Data availability
The datasets generated and/or analyzed during the current study are not publicly available due to the fact that this study forming part of an ongoing research program but are available from the corresponding author on reasonable request.
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Acknowledgements
This study was funded by the National Natural Science Foundation of China (Grant Nos. 62304132 and 62174074), the Science and Technology Commission of Shanghai Municipality (Grant No. 25JD1402100), the Shenzhen Fundamental Research Program (Grant No. JCYJ20220530115014032), the Zhujiang Young Talent Program (Grant No. 2021QN02X362), the Guangdong Provincial Department of Education Innovation Team Program (Grant No. 2021KCXTD012), SME-CIMCube Joint Lab, and the Guangdong Provincial Engineering Research Center of 3-D Integration. The funders played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. We would like to acknowledge the Center for Advanced Electronic Materials and Devices (AEMD) of Shanghai Jiao Tong University and the Core Research Facilities (CRF) at Southern University of Science and Technology for the facilities used and the technical support provided by their staff and engineers.
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X.F. and T.T. conceived and designed the experiments. T.T. and H.G. performed the device fabrication and electrical measurements. T.T, H.G, S.W., Y.W., Y.L and X.F. contributed to the discussion and results analysis. T.T. and X.F. wrote the manuscript.
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Tan, T., Guo, H., Wang, S. et al. Multi-stage Kalman filtering system for sensor fusion integrated with MoS2 memtransistor featuring 1024 conductance levels. npj 2D Mater Appl (2026). https://doi.org/10.1038/s41699-026-00672-7
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DOI: https://doi.org/10.1038/s41699-026-00672-7
