Low Power Optoelectronic Neuromorphic Memristor for In-Sensor Computing and Multilevel Hardware Security...

Conventional software-based encryption faces mounting limitations in power efficiency and security, inspiring the development of emerging neuromorphic computing hardware encryption. This study presents a hardware-level multi-dimensional encryption paradigm utilizing optoelectronic neuromorphic devices with low energy consumption of 3.3 fJ, exhibiting great potential in motion detection, in-sensor computing and multilevel encrypted information communication. By encoding ASCII characters into unique optical pulse sequences defined by wavelength, duration, and pulse number, the device transforms digital information into physically obfuscated electrical responses, thereby establishing a secure encryption mechanism. Based on neuromorphic response of optoelectronic device, convolutional neural network was trained to decrypt signals with recognition accuracy of 97.4% for legitimate users while maintaining robustness against unauthorized access (∼2.88% accuracy). To address complex real-world scenarios of maritime communication, dual-authentication "friend-or-foe" identification system was constructed with two-layer authentication. The neuromorphic optoelectronic system combines motion perception, real-time flag semaphore recognition via reservoir computing with multi-band photonic encryption, showing great potential in next-generation neuromorphic maritime communication.