A FeFET with a novel MFMFIS gate stack: towards energy-efficient and ultrafast NVMs for neuromorphic computing.

The discovery of ferroelectricity in the fluorite structure based hafnium oxide (HfO2) material sparked major efforts for reviving the ferroelectric field effect transistor (FeFET) memory concept. A Novel metal-ferroelectric-metal-ferroelectric-insulator-semiconductor (MFMFIS) FeFET memory is reported based on dual ferroelectric integration as an MFM and MFIS in a single gate stack using Si-doped Hafnium oxide (HSO) ferroelectric (FE) material. The MFMFIS top and bottom electrode contacts, dual HSO based ferroelectric layers, and tailored MFM to MFIS area ratio (AR-TB) provide a flexible stack structure tuning for improving the FeFET performance. The AR-TB tuning shows a tradeoff between the MFM voltage increase and the weaker FET Si channel inversion, particularly notable in the drain saturation currentID(sat)when the AR-TB ratio decreases. Dual HSO ferroelectric layer integration enables a maximized memory window (MW) and dynamic control of its size by tuning the MFM to MFIS switching contribution through the AR-TB change. The stack structure control via the AR-TB tuning shows further merits in terms of a low voltage switching for a saturated MW size, an extremely linear at wide dynamic range of the current update, as well as high symmetry in the long term synaptic potentiation and depression. The MFMFIS stack reliability is reported in terms of the switching variability, temperature dependence, endurance, and retention. The MFMFIS concept is thoroughly discussed revealing profound insights on the optimal MFMFIS stack structure control for enhancing the FeFET memory performance.

Compensation of disorder for extraordinary optical transmission effect in nanopore arrays fabricated by nanosphere photolithography.

The work considers the effect of extraordinary optical transmission (EOT) in polycrystalline arrays of nanopores fabricated via nanosphere photolithography (NPL). The use of samples with different qualities of polycrystalline structure allows us to reveal the role of disorder for EOT. We propose a phenomenological model which takes the disorder into account in numerical simulations and validate it using experimental data. Due to the NPL flexibility for the structure geometry control, we demonstrate the possiblity to partially compensate the disorder influence on EOT by the nanopore depth adjustments. The proposed experimental and theoretical results are promising to reveal the NPL limits for EOT-based devices and stimulate systematic studies of disorder compensation designs.

Surface Plasmon Resonance Based Temperature Sensors in Liquid Environment.

The aim of this work is to measure the temperature variations by analyzing the plasmon signature on a metallic surface that is periodically structured and immersed in a liquid. A change in the temperature of the sample surface induces a modification of the local refractive index leading to a shift of the surface plasmon resonance (SPR) frequency due to the strong interaction between the evanescent electric field and the metallic surface. The experimental set-up used in this study to detect the refractive index changes is based on a metallic grating permitting a direct excitation of a plasmon wave, leading to a high sensibility, high-temperature range and contactless sensor within a very compact and simple device. The experimental set-up demonstrated that SPR could be used as a non-invasive, high-resolution temperature measurement method for metallic surfaces.

Bottom-Up Assembly of Molecular Nanostructures by Means of Ferroelectric Lithography.

Here, we report on the photochemical deposition of Rhodamine 6G (Rh6G) and Alexa647 molecules from aqueous and methanolic solution along 180° ferroelectric (FE) domain walls (DWs) of z-cut lithium niobate (LNO) single crystals. Molecules and FE domains were investigated by means of dynamic-mode AFM, piezoresponse force microscopy (PFM), and confocal scanning fluorescence microscopy. A high deposition affinity for 180° DWs on the LNO surface is observed, leading to the formation of molecular nanowires. Additionally, a more complex deposition pattern for Rh6G adsorbed to the domain areas of freshly poled samples was equally observed, being associated with the DW dynamics. These results are explained by considering contributions from screening-charge-dependent photochemistry as confined to the DWs, UV-induced DW motion, and transient electrostatic fields arising from the metastable defect distribution shortly after poling. Hence, tuning these effects offers the possibility for accurately controlling the complex bottom-up assembly of functional molecular nanostructures through domain-structured ferroelectric templates.

Transverse-mode selective resonant grating-mirrors for high power and high brightness emission.

The finite angular spectral width of a 2D resonant grating mirror is adjusted to select the fundamental transverse mode of a laser and to filter out higher order modes. The selection principle is explained phenomenologically on a simplified 1D model. The 2D design is made so as to sustain the large field concentration in the grating slab-waveguide mirror, and the technology permitting to obtain the resonant reflection within the gain bandwidth of two types of laser is described. The blank experimental measurements by means of a white light supercontinuum are shown to match the targeted specifications on the resonance spectral position and angular width.

Ferroelectric compute-in-memory annealer for combinatorial optimization problems.

Computationally hard combinatorial optimization problems (COPs) are ubiquitous in many applications. Various digital annealers, dynamical Ising machines, and quantum/photonic systems have been developed for solving COPs, but they still suffer from the memory access issue, scalability, restricted applicability to certain types of COPs, and VLSI-incompatibility, respectively. Here we report a ferroelectric field effect transistor (FeFET) based compute-in-memory (CiM) annealer for solving larger-scale COPs efficiently. Our CiM annealer converts COPs into quadratic unconstrained binary optimization (QUBO) formulations, and uniquely accelerates in-situ the core vector-matrix-vector (VMV) multiplication operations of QUBO formulations in a single step. Specifically, the three-terminal FeFET structure allows for lossless compression of the stored QUBO matrix, achieving a remarkably 75% chip size saving when solving Max-Cut problems. A multi-epoch simulated annealing (MESA) algorithm is proposed for efficient annealing, achieving up to 27% better solution and ~ 2X speedup than conventional simulated annealing. Experimental validation is performed using the first integrated FeFET chip on 28nm HKMG CMOS technology, indicating great promise of FeFET CiM array in solving general COPs.

Deep random forest with ferroelectric analog content addressable memory.

Deep random forest (DRF), which combines deep learning and random forest, exhibits comparable accuracy, interpretability, low memory and computational overhead to deep neural networks (DNNs) in edge intelligence tasks. However, efficient DRF accelerator is lagging behind its DNN counterparts. The key to DRF acceleration lies in realizing the branch-split operation at decision nodes. In this work, we propose implementing DRF through associative searches realized with ferroelectric analog content addressable memory (ACAM). Utilizing only two ferroelectric field effect transistors (FeFETs), the ultra-compact ACAM cell performs energy-efficient branch-split operations by storing decision boundaries as analog polarization states in FeFETs. The DRF accelerator architecture and its model mapping to ACAM arrays are presented. The functionality, characteristics, and scalability of the FeFET ACAM DRF and its robustness against FeFET device non-idealities are validated in experiments and simulations. Evaluations show that the FeFET ACAM DRF accelerator achieves ∼106×/10× and ∼106×/2.5× improvements in energy and latency, respectively, compared to other DRF hardware implementations on state-of-the-art CPU/ReRAM.

THz Thin Film Varactor Based on Integrated Ferroelectric HfZrO2.

In this paper, we present a broadband microwave characterization of ferroelectric hafnium zirconium oxide (Hf0.5Zr0.5O2) metal-ferroelectric-metal (MFM) thin film varactor from 1 kHz up to 0.11 THz. The varactor is integrated into the back-end-of-line (BEoL) of 180 nm CMOS technology as a shunting capacitor for the coplanar waveguide (CPW) transmission line. At low frequencies, the varactor shows a slight imprint behavior, with a maximum tunability of 15% after the wake-up. In the radio- and mmWave frequency range, the varactor's maximum tunability decreases slightly from 13% at 30 MHz to 10% at 110 GHz. Ferroelectric varactors were known for their frequency-independent, linear tunability as well as low loss. However, this potential was never fully realized due to limitations in integration. Here, we show that ferroelectric HfO2 thin films with good back-end-of-line compatibility support very large scale integration. This opens up a broad range of possible applications in the mmWave and THz frequency range such as 6G communications, imaging radar, or THz imaging.

Ferroelectric Content-Addressable Memory Cells with IGZO Channel: Impact of Retention Degradation on the Multibit Operation.

Indium gallium zinc oxide (IGZO)-based ferroelectric thin-film transistors (FeTFTs) are being vigorously investigated for being deployed in computing-in-memory (CIM) applications. Content-addressable memories (CAMs) are the quintessential example of CIM, which conduct a parallel search over a queue or stack to obtain the matched entries for a given input data. CAM cells offer the ability for massively parallel searches in a single clock cycle throughout an entire CAM array for the input query, thereby enabling pattern matching and searching functionality. Therefore, CAM cells are used extensively for pattern matching or search operations in data-centric computing. This paper investigates the impact of retention degradation on IGZO-based FeTFT on the multibit operation in content CAM cell applications. We propose a scalable multibit 1FeTFT-1T-based CAM cell composed of only one FeTFT and one transistor, thus significantly improving the density and energy efficiency compared with conventional complementary metal-oxide-semiconductor (CMOS)-based CAM. We successfully demonstrate the operations of our proposed CAM with storage and search by exploiting the multilevel states of the experimentally calibrated IGZO-based FeTFT devices. We also investigate the impact of retention degradation on the search operation. Our proposed IGZO-based 3-bit and 2-bit CAM cell shows 104 s and 106 s retention, respectively. The single-bit CAM cell shows lifelong (10 years) retention.

Embedding security into ferroelectric FET array via in situ memory operation.

Non-volatile memories (NVMs) have the potential to reshape next-generation memory systems because of their promising properties of near-zero leakage power consumption, high density and non-volatility. However, NVMs also face critical security threats that exploit the non-volatile property. Compared to volatile memory, the capability of retaining data even after power down makes NVM more vulnerable. Existing solutions to address the security issues of NVMs are mainly based on Advanced Encryption Standard (AES), which incurs significant performance and power overhead. In this paper, we propose a lightweight memory encryption/decryption scheme by exploiting in-situ memory operations with negligible overhead. To validate the feasibility of the encryption/decryption scheme, device-level and array-level experiments are performed using ferroelectric field effect transistor (FeFET) as an example NVM without loss of generality. Besides, a comprehensive evaluation is performed on a 128 × 128 FeFET AND-type memory array in terms of area, latency, power and throughput. Compared with the AES-based scheme, our scheme shows ~22.6×/~14.1× increase in encryption/decryption throughput with negligible power penalty. Furthermore, we evaluate the performance of our scheme over the AES-based scheme when deploying different neural network workloads. Our scheme yields significant latency reduction by 90% on average for encryption and decryption processes.

First demonstration of in-memory computing crossbar using multi-level Cell FeFET.

Advancements in AI led to the emergence of in-memory-computing architectures as a promising solution for the associated computing and memory challenges. This study introduces a novel in-memory-computing (IMC) crossbar macro utilizing a multi-level ferroelectric field-effect transistor (FeFET) cell for multi-bit multiply and accumulate (MAC) operations. The proposed 1FeFET-1R cell design stores multi-bit information while minimizing device variability effects on accuracy. Experimental validation was performed using 28 nm HKMG technology-based FeFET devices. Unlike traditional resistive memory-based analog computing, our approach leverages the electrical characteristics of stored data within the memory cell to derive MAC operation results encoded in activation time and accumulated current. Remarkably, our design achieves 96.6% accuracy for handwriting recognition and 91.5% accuracy for image classification without extra training. Furthermore, it demonstrates exceptional performance, achieving 885.4 TOPS/W-nearly double that of existing designs. This study represents the first successful implementation of an in-memory macro using a multi-state FeFET cell for complete MAC operations, preserving crossbar density without additional structural overhead.

Narrow band, large angular width resonant reflection from a periodic high index grid at terahertz frequency.

The property of a thin silicon membrane with periodic air slits of definite depth and width to exhibit under normal incidence a close to 100% ultra-narrow band reflection peak is demonstrated experimentally in the terahertz frequency range on a single-crystal silicon grid fabricated by submillimeter microsystem technology. An analysis based on the true modes supported by the grid reveals the nature of such resonances and permits to sort out those exhibiting ultra-narrow band.

High efficiency, high selectivity ultra-thin resonant diffractive elements.

Resonant diffractive elements as the association of a surface corrugation with a surface wave exhibit boosted diffraction efficiency and high selectivity properties under the effect of ultra-shallow subwavelength surface reliefs. This is demonstrated by four examples of resonant functional structures made of very different material systems over the optical spectrum. All four structures are fabricated by slow wet etching as the inherent lateral broadening in corrugations of very small aspect ratio can be neglected.

Azimuthally polarized laser mode generation by multilayer mirror with wideband grating-induced TM leakage in the TE stopband.

A new intracavity laser polarization-mode selection scheme relying upon a TE/TM diffractive dichroism principle in a grating multilayer mirror is proposed and demonstrated. The grating diffracts the first orders between the TE and TM band edges of the angular spectra of the laser mirror inducing a leakage of the TM polarization into the mirror substrate through the multilayer stack whereas TE diffraction into the substrate is forbidden. This mechanism is non-resonant, thus relatively wide-band. Applied with a circular-line grating in the 1.0 µm - 1.1 µm wavelength range, this mirror filters out the radially polarization mode and causes the emission of the azimuthally polarized mode. An original amorphous silicon grating technology was developed and the optical function demonstrated in a Nd:YAG laser.

Achieving software-equivalent accuracy for hyperdimensional computing with ferroelectric-based in-memory computing.

Hyperdimensional computing (HDC) is a brain-inspired computational framework that relies on long hypervectors (HVs) for learning. In HDC, computational operations consist of simple manipulations of hypervectors and can be incredibly memory-intensive. In-memory computing (IMC) can greatly improve the efficiency of HDC by reducing data movement in the system. Most existing IMC implementations of HDC are limited to binary precision which inhibits the ability to match software-equivalent accuracies. Moreover, memory arrays used in IMC are restricted in size and cannot immediately support the direct associative search of large binary HVs (a ubiquitous operation, often over 10,000+ dimensions) required to achieve acceptable accuracies. We present a multi-bit IMC system for HDC using ferroelectric field-effect transistors (FeFETs) that simultaneously achieves software-equivalent-accuracies, reduces the dimensionality of the HDC system, and improves energy consumption by 826x and latency by 30x when compared to a GPU baseline. Furthermore, for the first time, we experimentally demonstrate multi-bit, array-level content-addressable memory (CAM) operations with FeFETs. We also present a scalable and efficient architecture based on CAMs which supports the associative search of large HVs. Furthermore, we study the effects of device, circuit, and architectural-level non-idealities on application-level accuracy with HDC.

Synergistic Approach of Interfacial Layer Engineering and READ-Voltage Optimization in HfO2-Based FeFETs for In-Memory-Computing Applications.

This article reports an improvement in the performance of the hafnium oxide-based (HfO2) ferroelectric field-effect transistors (FeFET) achieved by a synergistic approach of interfacial layer (IL) engineering and READ-voltage optimization. FeFET devices with silicon dioxide (SiO2) and silicon oxynitride (SiON) as IL were fabricated and characterized. Although the FeFETs with SiO2 interfaces demonstrated better low-frequency characteristics compared to the FeFETs with SiON interfaces, the latter demonstrated better WRITE endurance and retention. Finally, the neuromorphic simulation was conducted to evaluate the performance of FeFETs with SiO2 and SiON IL as synaptic devices. We observed that the WRITE endurance in both types of FeFETs was insufficient to carry out online neural network training. Therefore, we consider an inference-only operation with offline neural network training. The system-level simulation reveals that the impact of systematic degradation via retention degradation is much more significant for inference-only operation than low-frequency noise. The neural network with FeFETs based on SiON IL in the synaptic core shows 96% accuracy for the inference operation on the handwritten digit from the Modified National Institute of Standards and Technology (MNIST) data set in the presence of flicker noise and retention degradation, which is only a 2.5% deviation from the software baseline.

Structural and Electrical Response of Emerging Memories Exposed to Heavy Ion Radiation.

Hafnium oxide- and GeSbTe-based functional layers are promising candidates in material systems for emerging memory technologies. They are also discussed as contenders for radiation-harsh environment applications. Testing the resilience against ion radiation is of high importance to identify materials that are feasible for future applications of emerging memory technologies like oxide-based, ferroelectric, and phase-change random-access memory. Induced changes of the crystalline and microscopic structure have to be considered as they are directly related to the memory states and failure mechanisms of the emerging memory technologies. Therefore, we present heavy ion irradiation-induced effects in emerging memories based on different memory materials, in particular, HfO2-, HfZrO2-, as well as GeSbTe-based thin films. This study reveals that the initial crystallinity, composition, and microstructure of the memory materials have a fundamental influence on their interaction with Au swift heavy ions. With this, we provide a test protocol for irradiation experiments of hafnium oxide- and GeSbTe-based emerging memories, combining structural investigations by X-ray diffraction on a macroscopic, scanning transmission electron microscopy on a microscopic scale, and electrical characterization of real devices. Such fundamental studies can be also of importance for future applications, considering the transition of digital to analog memories with a multitude of resistance states.

Optimized electron beam writing strategy for fabricating computer-generated holograms based on an effective medium approach.

Recent research revealed that using the effective medium approach to generate arbitrary multi-phase level computer-generated holograms is a promising alternative to the conventional multi-height level approach. Although this method reduces the fabrication effort using one-step binary lithography, the subwavelength patterning process remains a huge challenge, particularly for large-scale applications. To reduce the writing time on variable shaped electron beam writing systems, an optimized strategy based on an appropriate reshaping of the binary subwavelength structures is illustrated. This strategy was applied to fabricate a three-phase level CGH in the visible range, showing promising experimental results.

Depth-minimized, large period half-wave corrugation for linear to radial and azimuthal polarization transformation by grating-mode phase management.

The transformation of the polarization distribution of a laser beam from linear to radial and azimuthal by means of a subwavelength binary corrugation etched in a high-index substrate faces fabrication difficulties and an inherent contradiction preventing the achievement of both conditions of 100% transmission and of π phase difference between polarization components. The contradiction is solved by resorting to an easily fabricable high-index corrugation on a low-index substrate where a larger period gives rise to grating-mode reflection/transmission phases that permit the fulfillment of both conditions with a depth-minimized corrugation. From the principle of the solution, a targeted numerical search gives the complete set of the corresponding shallow structures, achieving polarization rotation in a fitting analytical form versus normalized variables.

Electric field-induced crystallization of ferroelectric hafnium zirconium oxide.

Ferroelectricity in crystalline hafnium oxide thin films is strongly investigated for the application in non-volatile memories, sensors and other applications. Especially for back-end-of-line (BEoL) integration the decrease of crystallization temperature is of major importance. However, an alternative method for inducing ferroelectricity in amorphous or semi-crystalline hafnium zirconium oxide films is presented here, using the newly discovered effect of electric field-induced crystallization in hafnium oxide films. When applying this method, an outstanding remanent polarization value of 2P[Formula: see text] = 47 [Formula: see text]C/cm[Formula: see text] is achieved for a 5 nm thin film. Besides the influence of Zr content on the film crystallinity, the reliability of films crystallized with this effect is explored, highlighting the controlled crystallization, excellent endurance and long-term retention.