A low-thermal-budget MOSFET-based reservoir computing for temporal data classification

Abstract Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain. In this regard, reservoir computing (RC) framework, which leverages straightforward training methods and efficient temporal signal processing, has emerged as a promising scheme. While various physical reservoir devices, including ferroelectric, optoelectronic, and memristor-based systems, have been demonstrated, many still face challenges related to compatibility with mainstream complementary metal oxide semiconductor (CMOS) integration processes. This study introduced a silicon-based schottky barrier metal−oxide−semiconductor field effect transistor (SB-MOSFET), which was fabricated under low thermal budget and compatible with back-end-of-line (BEOL). The device demonstrated short-term memory characteristics, facilitated by the modulation of schottky barriers and charge trapping. Utilizing these characteristics, a RC system for temporal data processing was constructed, and its performance was validated in a 5 × 4 digital classification task, achieving an accuracy exceeding 98% after 50 training epochs. Furthermore, the system successfully processed temporal signal in waveform classification and prediction tasks using time-division multiplexing. Overall, the SB-MOSFET's high compatibility with CMOS technology provides substantial advantages for large-scale integration, enabling the development of energy-efficient reservoir computing hardware.