Photoferroelectric All-van-der-Waals Heterostructure for Multimode Neuromorphic Ferroelectric Transistors

Optical-Electrical Coordinately Modulated Memristor Based on 2D Ferroelectric RP Perovskite for Artificial Vision Applications

Traditional artificial vision systems built using separate sensing, computing, and storage units have problems with high power consumption and latency caused by frequent data transmission between functional units. An effective approach is to transfer some memory and computing tasks to the sensor, enabling the simultaneous perception-storage-processing of light signals. Here, an optical-electrical coordinately modulated memristor is proposed, which controls the conductivity by means of polarization of the 2D ferroelectric Ruddlesden-Popper perovskite film at room temperature. The residual polarization shows no significant decay after 109-cycle polarization reversals, indicating that the device has high durability. By adjusting the pulse parameters, the device can simulate the bio-synaptic long/short-term plasticity, which enables the control of conductivity with a high linearity of ≈0.997. Based on the device, a two-layer feedforward neural network is built to recognize handwritten digits, and the recognition accuracy is as high as 97.150%. Meanwhile, building optical-electrical reserve pool system can improve 14.550% for face recognition accuracy, further demonstrating its potential for the field of neural morphological visual systems, with high density and low energy loss.

Applying the Wake-Up-like Effect to Enhance the Capabilities of Two-Dimensional Ferroelectric Field-Effect Transistors

For traditional ferroelectric field-effect transistors (FeFETs), enhancing the polarization domain of bulk ferroelectric materials is essential to improve device performance. However, there has been limited investigation into the enhancement of polarization field in two-dimensional (2D) ferroelectric material such as CuInP2S6 (CIPS). In this study, similar to bulk ferroelectric materials, CIPS exhibited enhanced polarization field upon application of external cyclic voltage. Moreover, unlike traditional ferroelectric materials, the polarization enhancement of CIPS is not due to redistribution of the defect but rather originates from a mechanism: the long-distance migration of Cu ions. We termed this mechanism the "wake-up-like effect". After incorporating the wake-up-like effect into the graphene/CIPS/WSe2 FeFET device, we successfully increased the hysteresis window and enhanced the current on/off ratio by 4 orders of magnitude. Moreover, the FeFET yielded remarkable achievements, such as multilevel nonvolatile memory with 21 distinct conductance levels, a high on/off ratio exceeding 106, a long retention time exceeding 103 s, and neuromorphic computing with 93% accuracy at recognizing handwritten digits. Introducing the wake-up-like effect to 2D CIPS may pave the way for innovative approaches to achieve advanced multilevel nonvolatile memory and neuromorphic computing capabilities for next-generation micro-nanoelectronic devices.

Graphene Magnetoresistance Control by Photoferroelectric Substrate

Ultralow dimensionality of 2D layers magnifies their sensitivity to adjacent charges enabling even postprocessing electric control of multifunctional structures. However, functionalizing 2D layers remains an important challenge for on-demand device-property exploitation. Here we report that an electrical and even fully optical way to control and write modifications to the magnetoresistive response of CVD-deposited graphene is achievable through the electrostatics of the photoferroelectric substrate. For electrical control, the ferroelectric polarization switch modifies graphene magnetoresistance by 67% due to a Fermi level shift with related modification in charge mobility. A similar function is also attained entirely by bandgap light due to the substrate photovoltaic effect. Moreover, an all-optical way to imprint and recover graphene magnetoresistance by light is reported as well as magnetic control of graphene transconductance. These findings extend photoferroelectric control in 2D structures to magnetic dimensions and advance wireless operation for sensors and field-effect transistors.

Single Wavelength Operating Neuromorphic Device Based on a Graphene-Ferroelectric Transistor

As global data generation continues to rise, there is an increasing demand for revolutionary in-memory computing methodologies and efficient machine learning solutions. Despite recent progress in electrical and electro-optical simulations of machine learning devices, the all-optical nonthermal function remains challenging, with single wavelength operation still elusive. Here we report on an optical and monochromatic way of neuromorphic signal processing for brain-inspired functions, eliminating the need for electrical pulses. Multilevel synaptic potentiation-depression cycles are successfully achieved optically by leveraging photovoltaic charge generation and polarization within the photoferroelectric substrate interfaced with the graphene sensor. Furthermore, the demonstrated low-power prototype device is able to reproduce exact signal profile of brain tissues yet with more than 2 orders of magnitude faster response. The reported properties should trigger all-optical and low power artificial neuromorphic development based on photoferroelectric structures.

Reconfigurable Multifunctional van der Waals Ferroelectric Devices and Logic Circuits

Emerging reconfigurable devices are fast gaining popularity in the search for next-generation computing hardware, while ferroelectric engineering of the doping state in semiconductor materials has the potential to offer alternatives to traditional von-Neumann architecture. In this work, we combine these concepts and demonstrate the suitability of reconfigurable ferroelectric field-effect transistors (Re-FeFETs) for designing nonvolatile reconfigurable logic-in-memory circuits with multifunctional capabilities. Modulation of the energy landscape within a homojunction of a 2D tungsten diselenide (WSe2) layer is achieved by independently controlling two split-gate electrodes made of a ferroelectric 2D copper indium thiophosphate (CuInP2S6) layer. Controlling the state encoded in the program gate enables switching between p, n, and ambipolar FeFET operating modes. The transistors exhibit on-off ratios exceeding 106 and hysteresis windows of up to 10 V width. The homojunction can change from Ohmic-like to diode behavior with a large rectification ratio of 104. When programmed in the diode mode, the large built-in p-n junction electric field enables efficient separation of photogenerated carriers, making the device attractive for energy-harvesting applications. The implementation of the Re-FeFET for reconfigurable logic functions shows how a circuit can be reconfigured to emulate either polymorphic ferroelectric NAND/AND logic-in-memory or electronic XNOR logic with a long retention time exceeding 104 s. We also illustrate how a circuit design made of just two Re-FeFETs exhibits high logic expressivity with reconfigurability at runtime to implement several key nonvolatile 2-input logic functions. Moreover, the Re-FeFET circuit demonstrates high compactness, with an up to 80% reduction in transistor count compared to standard CMOS design. The 2D van de Waals Re-FeFET devices therefore exhibit promising potential for both More-than-Moore and beyond-Moore future of electronics, in particular for an energy-efficient implementation of in-memory computing and machine learning hardware, due to their multifunctionality and design compactness.

Voltage-Mode Ferroelectric Synapse for Neuromorphic Computing

Ferroelectric materials with a modulable polarization extent hold promise for exploring voltage-driven neuromorphic hardware, in which direct current flow can be minimized. Utilizing a single active layer of an insulating ferroelectric polymer, we developed a voltage-mode ferroelectric synapse that can continuously and reversibly update its states. The device states are straightforwardly manifested in the form of variable output voltage, enabling large-scale direct cascading of multiple ferroelectric synapses to build a deep physical neural network. Such a neural network based on potential superposition rather than current flow is analogous to the biological counterpart driven by action potentials in the brain. A high accuracy of over 97% for the simulation of handwritten digit recognition is achieved using the voltage-mode neural network. The controlled ferroelectric polarization, revealed by piezoresponse force microscopy, turns out to be responsible for the synaptic weight updates in the ferroelectric synapses. The present work demonstrates an alternative strategy for the design and construction of emerging artificial neural networks.

Engineering Ferroelectricity and Large Piezoelectricity in h-BN

Hexagonal boron nitride (h-BN) is a well-known layered van der Waals (vdW) material that exhibits no spontaneous electric polarization due to its centrosymmetric structure. Extensive density functional theory (DFT) calculations are used to demonstrate that doping through the substitution of B by isovalent Al and Ga breaks the inversion symmetry and induces local dipole moments along the c-axis, which promotes a ferroelectric (FE) alignment over antiferroelectric. For doping concentrations below 25%, a "protruded layered" structure in which the dopant atoms protrude out of the planar h-BN layers is energetically more stable than the flat layered structure of pristine h-BN or a wurtzite structure similar to w-AlN. The computed polarization, between 7.227 and 21.117 μC/cm2, depending on dopant concentration and the switching barrier (16.684 and 45.838 meV/atom) for the FE polarization reversal are comparable to that of other well-known FEs. Interestingly, doping of h-BN also induces a large negative piezoelectric response in otherwise nonpiezoelectric h-BN. For example, we compute d33 of -24.214 pC/N for Ga0.125B0.875N, which is about 5 times larger than that of pure w-AlN (5 pC/N), although the computed e33 (-1.164 C/m2) is about 1.6 times lower than that of pure w-AlN (1.462 C/m2). Because of the layered structure, the rather small elastic constant C33 provides the origin of the large d33. Moreover, doping makes h-BN an electric auxetic piezoelectric. We also show that ferroelectricity in doped h-BN may persist down to its trilayer, which indicates high potential for applications in FE nonvolatile memories.

2D Ferroic Materials for Nonvolatile Memory Applications

The emerging nonvolatile memory technologies based on ferroic materials are promising for producing high-speed, low-power, and high-density memory in the field of integrated circuits. Long-range ferroic orders observed in 2D materials have triggered extensive research interest in 2D magnets, 2D ferroelectrics, 2D multiferroics, and their device applications. Devices based on 2D ferroic materials and heterostructures with an atomically smooth interface and ultrathin thickness have exhibited impressive properties and significant potential for developing advanced nonvolatile memory. In this context, a systematic review of emergent 2D ferroic materials is conducted here, emphasizing their recent research on nonvolatile memory applications, with a view to proposing brighter prospects for 2D magnetic materials, 2D ferroelectric materials, 2D multiferroic materials, and their relevant devices.