A ferroelectric semiconductor field-effect transistor (FeS-FET) was proposed and experimentally demonstrated for the first time. In this novel FeS-FET, a two-dimensional (2D) ferroelectric semiconductor α-In2Se3 is used to replace conventional semiconductor as channel.α-In2Se3 is identified due to its proper bandgap, room temperature ferroelectricity, the ability to maintain ferroelectricity down to a few atomic layers and the feasibility for large-area growth.An atomic-layer deposition (ALD) Al2O3 passivation method was developed to protect and enhance the performance of the α-In2Se3 FeS-FETs. The fabricated FeS-FETs exhibit high performance with a large memory window, a high on/off ratio over 10 8 , a maximum on-current of 671 μA/μm, high electron mobility of 488 cm 2 /V•s, and the potential to exceed the existing Fe-FETs for non-volatile memory applications.

A material with reversible temperature change capability under an external electric field, known as the electrocaloric effect (ECE), has long been considered as a promising solid-state cooling solution. However, electrocaloric (EC) performance of EC materials generally is not sufficiently high for real cooling applications. As a result, exploring EC materials with high performance is of great interest and importance. Here, we report on the ECE of ferroelectric materials with van der Waals layered structure (CuInP2S6 or CIPS in this work in particular).Over 60% polarization charge change is observed within a temperature change of only 10 K at Curie temperature. Large adiabatic temperature change (|ΔT|) of 3.3 K, isothermal entropy change (|ΔS|) of 5.8 J kg -1 K -1 at |ΔE|=142.0 kV cm -1 at 315 K (above and near room temperature) are achieved, with a large EC strength (|ΔT|/|ΔE|) of 29.5 mK cm kV -1 . The ECE of CIPS is also investigated theoretically by numerical simulation and a further EC performance projection is provided.Electrocaloric refrigerators using electrocaloric materials are low noise, environmentfriendly and can be scaled down to small dimensions, compared to the common vaporcompression refrigerators. 1-13 Electrocaloric cooling is also much easier and lower cost to realize compared to other field induced cooling techniques such as magnetocaloric and mechanocaloric cooling, because the electric field is easily to be realized and accessible. Thus, electrocaloric effect is promising for future cooling applications, especially in micro-or nano-scale such as onchip cooling. Electrocaloric effect in ferroelectric materials is of special interest because of the large polarization change near the ferroelectric-paraelectric (FE-PE) phase transition temperature 21 TOC.

Historically, memory technologies have been evaluated based on their storage density, cost, and latencies. Beyond these metrics, the need to enable smarter and intelligent computing platforms at a low area and energy cost has brought forth interesting avenues for exploiting non-volatile memory (NVM) technologies. In this paper, we focus on non-volatile memory technologies and their applications to bio-inspired neuromorphic computing, enabling spike-based machine intelligence. Spiking neural networks (SNNs) based on discrete neuronal “action potentials” are not only bio-fidel but also an attractive candidate to achieve energy-efficiency, as compared to state-of-the-art continuous-valued neural networks. NVMs offer promise for implementing both area- and energy-efficient SNN compute fabrics at almost all levels of hierarchy including devices, circuits, architecture, and algorithms. The intrinsic device physics of NVMs can be leveraged to emulate dynamics of individual neurons and synapses. These devices can be connected in a dense crossbar-like circuit, enabling in-memory, highly parallel dot-product computations required for neural networks. Architecturally, such crossbars can be connected in a distributed manner, bringing in additional system-level parallelism, a radical departure from the conventional von-Neumann architecture. Finally, cross-layer optimization across underlying NVM based hardware and learning algorithms can be exploited for resilience in learning and mitigating hardware inaccuracies. The manuscript starts by introducing both neuromorphic computing requirements and non-volatile memory technologies. Subsequently, we not only provide a review of key works but also carefully scrutinize the challenges and opportunities with respect to various NVM technologies at different levels of abstraction from devices-to-circuit-to-architecture and co-design of hardware and algorithm.

In this paper, we describe and analytically substantiate an alternate explanation for the negative capacitance (NC) effect in ferroelectrics (FE). We claim that the NC effect previously demonstrated in resistance-ferroelectric (R-FE) networks does not necessarily validate the existence of “S” shaped relation between polarization and voltage (according to Landau theory). In fact, the NC effect can be explained without invoking the “S”-shaped behavior of FE. We employ an analytical model for FE (Miller model) in which the steady state polarization strictly increases with the voltage across the FE and show that despite the inherent positive FE capacitance, reduction in FE voltage with the increase in its charge is possible in a R-FE network as well as in a ferroelectric-dielectric (FE-DE) stack. This can be attributed to a large increase in FE capacitance near the coercive voltage coupled with the polarization lag with respect to the electric field. Under certain conditions, these two factors yield transient NC effect. We analytically derive conditions for NC effect in R-FE and FE-DE networks. We couple our analysis with extensive simulations to explain the evolution of NC effect. We also compare the trends predicted by the aforementioned Miller model with Landau-Khalatnikov (L-K) model (static negative capacitance due to “S”-shape behaviour) and highlight the differences between the two approaches. First, with an increase in external resistance in the R-FE network, NC effect shows a non-monotonic behavior according to Miller model but increases according to L-K model. Second, with the increase in ramp-rate of applied voltage in the FE-DE stack, NC effect increases according to Miller model but decreases according to L-K model. These results unveil a possible way to experimentally validate the actual reason of NC effect in FE.

In this work, we investigate the accumulative polarization (P) switching characteristics of ferroelectric (FE) thin films under the influence of sequential electric-field pulses. By developing a dynamic phase-field simulation framework based on time-dependent Landau-Ginzburg model, we analyze P excitation and relaxation characteristics in FE. In particular, we show that the domain-wall instability can cause different spontaneous P-excitation/relaxation behaviors that, in turn, can influence Pswitching dynamics for different pulse sequences. By assuming a local and global distribution of coercive field among the grains of an FE sample, we model the P-accumulation process in Hf 0.4 Zr 0.6 O 2 (HZO) and its dependency on applied electric field and excitation/relaxation time. According to our analysis, domain-wall motion along with its instability under certain conditions plays a pivotal role in accumulative P-switching and the corresponding excitation and relaxation characteristics.Ferroelectric (FE) materials, particularly Zr doped HfO 2 (Hf 1-x Zr x O 2 :HZO 1 ) have drawn significant research interest in recent times due to CMOS process compatibility 2 , thickness scalability 3,4 as well as many promising attributes of ferroelectric field effect transistors 2,5 (FEFETs) for low-power logic 5-8 and non-volatile memories 9,10 applications. In addition, FEFETs can provide multiple non-volatile resistive states that harness the multi-domain FE characteristics, leading to the possibilities for multi-bit synapses 11,12 in a neuromorphic hardware 13 . Further, newly reported accumulative polarization (P)-switching process 14 in ultra-thin FE leads to many appealing opportunities for novel applications like correlation detection 15 and other non-Boolean computing paradigms 16 . For such emerging applications of FEFETs, the P-switching dynamics in response to sub/super-coercive voltage pulse trains play an important role and are, therefore, critical to understand.To that effect, this letter analyzes spatially local Pswitching dynamics and its participation in globally observable P-accumulation characteristics in response to a pulse train. Our analysis is based on a dynamic phase field model 17,18 coupled with measured accumulation characteristics of HZO. By providing the spatial distribution of P (Pmap) in different electric field (E-field) excitation and relaxation steps, we discuss different types of P excitation and relaxation processes and their corresponding dependency on Efield (E) amplitude (E app max ), ON time (or excitation time T on ) and OFF time of the pulse (or relaxation time T o f f ). Finally, considering a coercive-field distribution among different FE grains, we analyze the experimental P-accumulation characteristics by utilizing our simulation framework.Let us start by describing the experimentally observed Pswitching characteristics in HZO. Fig. 1(a) shows the measured charge vs. E-field (Q-E) characteristics of a 10nm HZO film (x=0.6, grown by ALD with TiN capping layer as top and bottom contact). He...

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