Electron devices using ferroelectric materials have recently stirred a strong interest as a technology booster for future integrated systems, since the discovery of HfO2-based ferroelectrics [1], which are CMOS-friendly and scalable. At present, a variety of devices using HfO2-based ferroelectrics such as FeRAM, FeFETs and FTJ have been extensively studied for low power memories and computing-in-memory applications. This enthusiasm has been enhanced by the increasing demands and strong concerns of AI calculations, which needs coexistence of both logic and memory functions. Thus, we are currently investigating the possibilities of FeRAM and FeFETs using Hf0.5Zr0.5O2 (HZO) films from the viewpoints of ultralow power memory and AI applications. For FeRAM, we have examined the possibility of low voltage operation and resulting thinning HZO films under low thermal budget needed for formation in BEOL for embedded non-volatile memory applications under advanced technology nodes [2]. However, we have found that there is a trade-off between crystallization temperature and HZO film thickness less than 6 nm. We have demonstrated that MFM capacitors with HZO less than 5 nm can realize crystallization temperature lower than 500ºC, excellent ferroelectricity (2Pr > 25 mC/cm2), low operating voltage (0.7-1.2 V), and high read/write endurance by performing sufficient amounts of wake-up operations to the thin HZO films. For FeFETs, we have quantitatively studied the behaviors of free carriers and the trapping around MFIS interfaces in order to well understand the FeFET memory operation and the reliability, on which the MFIS interfaces have a crucial influence [3-6]. We have clarified that a large amount of electron trapping near the HZO/interfacial layer interfaces can help polarization of HZO while hole trapping is less significant, inducing asymmetric P-V loops in the FeFET memory operation, by employing quasi-static split C-V and Hall measurements to Si FeFETs. This electron trapping properties can significantly affect the memory window, the read-out operation and the FeFET reliability. On the other hand, the complicated dynamics of the FeFET operation including polarization switching and carrier trapping can provide a possibility to utilize this device as AI calculation hardware. We have proposed a new AI calculation scheme by reservoir computing using FeFETs for neuromorphic applications [7, 8]. Here, reservoir computing, which is one of recurrent neural networks [9, 10], is known as promising for AI calculations with low-power consumption, real-time learning and high potential to time-series data processing. Also, reservoir computing by any physical systems holding short-term memory and non-linear response functions can lead to further improvement in energy efficiency of AI calculations [11]. FeFETs with the memory effects due to polarization and the non-linearity due to the threshold operation and the complicated interactions between domains and between domains/channels are eligible as physical reservoirs. We have experimentally demonstrated memory and parity check task operations of time-series input data and the ability to classify them by taking time responses of the drain current for gate voltage input as the virtual nodes. As a result, reservoir computing systems based on Si FeFETs are expected to provide CMOS-platform-friendly hardware for neuromorphic AI calculations with low power consumption, low cost and fast learning time.This work was supported by JST CREST (JPMJCR20C3), JSPS KAKENHI (19K15021), and Nanotechnology Platform project by MEXT (JPMXP09A20UT0056), Japan.