Multi-stage Kalman filtering system for sensor fusion integrated with MoS2 memtransistor featuring 1024 conductance levels

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.