Dynamic FET-based memristor with relaxor antiferroelectric HfO2 gate dielectric for fast reservoir computing

Reservoir computing (RC), as a framework for artificial intelligence (AI) computation, is derived from recurrent neural networks, but with higher efficiency benefits from its much simpler training process. A hardware-based physical RC employs a reservoir that is a fixed non-linear system, and in a simple RC structure, the reservoir is ideally a short-term dynamic field effect transistor-based memristor. In this work, we demonstrate that reservoir computing employing a polarization-modulated transistor as a physical reservoir with a relaxor antiferroelectric back gate significantly reduces parameters, power consumption, and calculation steps of the neural network. Here, relaxor antiferroelectricity is realized in an Al-doped Hf0.5Zr0.5O2 (Al:HZO) thin film, and with the poling process-sensitive modulation of the Al:HZO gate dielectric, a dynamic and polarization field-modulated transistor is demonstrated as a short-term memristor. This short-term memory behavior is further used to encode binary images to form a reservoir for fast image processing, where the reservoir mimics small convolution operations to convert images. This reservoir computing is also applied to image classification, denoising, and similarity judgment, and very reliable results are obtained. This work provides a new approach to hardware-based physical reservoir computing.