Back-End, CMOS-Compatible Ferroelectric FinFET for Synaptic Weights

1 Introduction

The computing capability of classical digital computers, based on Complementary Metal Oxide Semiconductor (CMOS) transistors, has advanced considerably in the past decades, mainly due to the shrinking down of transistor’s dimensions, as predicted by Moore’s law (Moore, 1965). The advent of the Artificial Intelligence (AI) has imposed critical requirements in terms of energy efficiency and processing speed, to address ambitious problems such as speech and image recognition (Gokmen and Vlasov, 2016). Conventional von Neuman architectures face two main challenges: first, Moore’s law is slowing down (due to rising fabrication cost and physical limitations), second, their performance is limited by the data transfer between the processor and the memory (Wong and Salahuddin, 2015). Brain-inspired neuromorphic architectures, allowing to perform computing at the site where data is stored, hence in-memory, are promising candidates to overcome this issue (Poon and Zhou, 2011). Such architectures consist of a collection of artificial neurons interconnected by plastic synapses in a crossbar topology which allows to efficiently perform the multiply and accumulate operation (Gokmen and Vlasov, 2016), a key computing task in neural networks (Kim et al., 2017; Yu, 2018). To imitate the biological synaptic plasticity, an analog programming capability of these synapses is required to define the synaptic weight. To achieve densely integrated neuromorphic circuits, both the material and the processes of the synaptic devices are required to be compatible with modern CMOS technology. Several technology implementations and physical phenomena, such as Phase-Change Memory (PCM) (Lacaita, 2006; Raoux et al., 2010; Boybat et al., 2018), filamentary-based Resistive Random Access Memory (RRAM) (Baek et al., 2004; Lee et al., 2008; Waser et al., 2009) and Electro-Chemical Memory (ECRAM) (Fuller et al., 2017; Kim et al., 2019; Tang et al., 2019), can lead to synaptic behavior, but they all rely on structural modification of the active materials involved. The recent discovery of the ferroelectric properties in hafnia composites (Böscke et al., 2011), a material already present in CMOS technology, has attracted further scientific interest in the field of neuromorphic hardware based on ferroelectrics. Three main classes of devices exploiting ferroelectricity for synaptic as well as neuronal functionalities were demonstrated in the past: the two-terminal Ferroelectric Tunneling Junctions (FTJs) (Ambriz-Vargas et al., 2017; Tian and Toriumi, 2017; Chen et al., 2018a; Goh and Jeon, 2018; Yu et al., 2021), the three-terminal Ferroelectric Field-Effect Transistors (FeFETs) (Mulaosmanovic et al., 2017; Sharma et al., 2017; Krivokapic et al., 2018; Zeng et al., 2018; Mo et al., 2019) and the two-terminal Ferroelectric Photovoltaic (FePv) synapses (Cheng et al., 2020; Cui et al., 2021). Although, both FTJs and FeFETs have been extensively investigated recently, showing large dynamic ranges, low energy dissipation, and synaptic functions including short and long term plasticity as well as Spike-Timing-Dependent Plasticity (STDP) (Nishitani et al., 2012; Boyn et al., 2017; Chen et al., 2018a; Guo et al., 2018; Majumdar et al., 2019; Li et al., 2020) the FePv devices, based on the polarization control of the photovoltaic behavior that exploit the photoresponsivity as synaptic weight, were used for binary data storage (Guo et al., 2013) and recently as prototype synapse (Cheng et al., 2020). While in state of the art hafnia-based two-terminal synaptic weights, the small current flowing through the ferroelectric layer limits their scalability (Begon-Lours et al., 2021), three terminal devices have the advantage of separating the write process (through the high impedence gate) and the read process (through the channel). Hafnia-based FeFET devices exploiting Si as channel material and implemented on the Front End Of Line (FEOL) were demonstrated as artificial neurons (Mulaosmanovic et al., 2018). However, the FEOL integration imposes constraints on the device footprint, and limits the design flexibility. The Back End Of Line (BEOL) integration is advantageous, by allowing for a larger device area, which in turn leads to a larger number of ferroelectric domains and, hence, an improved analog (multi-level) behavior. Planar state of the art BEOL three-terminal synaptic devices based on HfZrO₄ (HZO) and utilizing a tungsten oxide (WO_x) channel, were realized in the past (Halter et al., 2020). However, in the last decade, the tri-gate technology (Lawrence and RUBIA, 2015) has replaced the planar one and allowed further CMOS transistor scaling. In this architecture, the gate surrounds the channel on three sides, creating a multigate device known as FinFET, with better gate-channel control and a smaller footprint with respect to a planar technology. In this work, we report on a scaled tri-gate FeFET (FinFeFET) having an overall footprint scaled down to four orders of magnitude with respect to (Halter et al., 2020). Being a Junction-Less Transistor (JLT) (Colinge et al., 2010; Colinge et al., 2011), no high-temperature source and drain implantation and annealing processes are required during the fabrication. The synaptic behavior is achieved through the partial polarization switching in HZO, which is used to electrostatically deplete or accumulate free carriers in the WO_x fins. We demonstrate the scaling of the ferroelectric technology down to device having 0.002 µm² area, and study the impact of the layout on the channel resistance, the influence of the fin’s geometry on the dynamic range, the retention, the analog behavior as well as the continuous and linear synaptic weight modulation. Moreover, both the process and the materials exploited are compatible with CMOS technology, the proposed synaptic element is promising for large-scale and densely integrated neuromorphic hardware based on ferroelectrics.

2 Results and Discussion

3D FeFET devices based on a W/TiN/HZO gate stack and 30 nm high WO_x fins were designed and fabricated using a process BEOL compatible, not exceeding 400°C. To investigate the effect of the layout on the device performances, several geometries of FinFeFETs were processed to find out the best trade-off in terms of fin’s length, width and number. Fins of 4 nm, 8 and 10 nm width were explored, and for each of them, two different lengths, 200 and 500 nm, respectively, and configurations with 1, 5, 10, 20, and 40 parallel fins were fabricated. The substoichiometric and amorphous WO_x channel, deposited by a Plasma-Enhanced Atomic Layer Deposition (PEALD) process at 375°C, was first crystallized and oxidized by annealing in an oxygen atmosphere, and then structured into fins. The source and drain contacts were deposited on the WO_x channel through lift-off. Then the TiN/HZO stack was grown, and the ferroelectric crystallization of the latter was performed using a millisecond flash lamp anneal at 375°C. The device was encapsulated by a 5 nm of Al₂O₃ and a 100 nm of SiO₂ passivation layers. Contact pads were formed on top of the passivation layers and routed through openings to source, drain and gate. The detailed processing steps can be found in the Sub-section 4.1. In Figure 1A the result of the fabrication process after the FinFeFET contact lift-off step was imaged by a Scanning Electron Microscope (SEM). SEM analysis of the fins revealed that the targeted widths of 4 and 8 nm both resulted in an approximately 10 nm wide fin after the transfer of the design from the resist to the WO_x. This is the result of cross exposure of dense structures close to the resists resolution limit. The materials properties were characterized by Grazing-Incidence X-Rays Diffraction (GIXRD). Figure 1B shows the GIXRD (ω = 0.44°) performed after HZO crystallization by ms-flash lamp annealing: the peak at 2θ = 30.8° corresponds to the overlap between the (111) peak of the orthorhombic (ferroelectric) phase and the (011) peak of the tetragonal phases of HZO (Park et al., 2013). As a consequence of the low temperature crystallization technique (O’Connor et al., 2018), no monoclinic HZO phase (peaks at 28.2° and 31.8°) (Materlik et al., 2015a) is observed in our sample. The additional peaks at 28.6°, 33.6° and 34.5° can be attributed to (111), (202) and (220) Miller indices of the tetragonal P42₁m phase of WO₃ (Jain et al., 2013), respectively. The peak at ≃ 36° is a combination of the multiple peaks from the orthorhombic and tetragonal phases of HZO. Figure 1C shows the two cross-section illustrations of the FinFeFET and its relative process flow. The resistive switching of HZO—WO_x FinFeFETs was investigated through electrical characterization. Oxygen vacancies confer n-type semiconducting properties to sub-stoichiometric WO_x<3 (Salje and Güttler, 1984). When the HZO ferroelectric remanent polarization points toward the interface with WO_x, free carriers accumulate at the interface to screen the polarization charges in HZO, thus the channel resistance R_SD decreases, and the memristor is in its Low Resistive State (LRS). By contrary, when the remanent polarization points toward the TiN interface, carrier depletion occurs in tungsten oxide at the interface with HZO, causing an increase of the channel resistance R_SD and resulting in a High Resistive State (HRS). The schematic energy band diagrams at the equilibrium of the TiN/HZO/WO_x stack, both in depletion (HRS) and accumulation (LRS) states, are shown in Figure 1D.

FIGURE 1

FIGURE 1. (A) SEM image after the source and drain contacts lift-off of a FinFeFET having L = 500 nm, N = 20 and W = 10 nm. (B) GIXRD for a diffraction angle (2θ) from 27° to 38° showing the presence of the characteristic peaks at 30.5° and 30.8° of the orthorhombic and tetragonal crystalline phases in HZO and the presence of the crystalline WO_x. (C) The cross-section sketches of the FinFeFET after stage 24, and its relative process flow, are provided. (D) Schematic energy band diagrams in depletion (HRS) and accumulation (LRS) states.

Polarization charges are screened in the HRS state and it is possible to define a screening length (depletion width) x_d, representing the thickness of the channel where the resistance is modulated. By decreasing the carrier density $n_{W{O}_x}$, the depletion width x_d is increased (Davis, 1973). Considering the FinFeFET devices with a single fin, the overall channel resistance R_SD can be thought as the resistance of two channels in parallel: one of thickness x_d on the outside of the fin, in which the resistivity is modulated upon polarization switching, and a bulky one which extends in the core of the fin, with a constant resistivity (Bégon-Lours et al., 2018; Halter et al., 2020). In multi-fin FeFETs, the equivalent channel resistance R_SD can be looked at as multiple single-fin resistances in parallel. For neuromorphic applications a large dynamic range (HRS/LRS), as well as multiple (analog) levels of the channel resistance, an absolute resistance in the tens of megohm range, good retention properties, a low device-to-device and cycle-to-cycle variation, a linear and symmetric weight-update rule, and a low power consumption are important characteristics of ideal memristors (Yu, 2018). The exact requirements vary depending on the application and from one implementation to the other. HZO is polycrystalline and the lateral domain size of HZO films prepared in the same conditions (O’Connor et al., 2018; Halter et al., 2020) is found to be ≃ 10 nm: a single fin is interfaced with several hundreds of domains. Switching only a subset of them allows the analog modulation of the channel resistance. To quantify the range of the pristine resistance from device to device, DC-electrical characterization was performed. A voltage sweep back and forth between −0.21 and 0.21 V, with a step of 20 mV, was applied between the source and the drain, keeping the gate floating. The electrical transport in the pristine state of FinFeFETs having different number of fins, is reported in Figure 2A. The current density J is calculated from the measured current I by $J=\frac{I}{N_{fins}\cdot h_{fins}\cdot w_{fins}}$, where N_fins, h_fins and w_fins are the number, the height and the width of the fins, respectively. For all the configurations, log(J) depends linearly on log(V) with a slope of 1, showing that the conduction in the channel is Ohmic. It depends on the carrier density by the relation (Sze and Ng, 2006):

$\log \left(J\right)=\log \left(\frac{q\cdot \mu \cdot n_{W{O}_x}}{L_{fins}}\right)+\log \left(V\right)(1)$

where q is the elementary charge, μ the electron mobility, and $n_{W{O}_x}$ the carrier density in the channel. Averaging the intercepts showed in Figure 2A and exploiting Eq. 1, the extracted μ ⋅ n product in WO_x is 1.87×10²⁰ (cmVs)⁻¹. In absence of specific structures to perform Hall measurements, it was not possible to determine the electron mobility in the channel. However, using the value of the WO_x mobility (${\mu }_{W{O}_x}$ = 0.19 cm²/V s) extracted by (Halter et al., 2020), the estimated carrier concentration is $n_{W{O}_x}$= 9.84 × 10²⁰ cm⁻³, which is coherent with the n-type semiconducting properties of the sub-stoichiometric WO_x<3. The remanent polarization P_r of 10 nm HZO was measured on TiN/WO_x/HZO/TiN capacitors on a different sample by Positive-Up Negative-Down (PUND) measurements (see Sub-section 4.3) and found to be P_r− = −11.2 µC/cm² and P_r+ = 17.7 µC/cm² (see Figure 2B). The asymmetry between positive and negative remanent polarization (|P_r+| > |P_r−|) indicate partially switched domains due to incomplete screening by the depleted WO_x layer. This results in a depolarization field across HZO (Mehta et al., 1973). Using the polarization as total charge per unit area (Q_s) we can define the electric field (E_interface) induced by it at the HZO/WO_x interface by using Gauss law (Brotherton, 2013):

$P_{r-}=Q_s=-{ϵ}_0\cdot {ϵ}_{HZO}\cdot E_{interface}(2)$

where ϵ_HZO is the permittivity of HZO, which for the ferroelectric phase is 29.1 (Materlik et al., 2015b). The depletion width x_d in WO_x with respect to the electric field caused by the polarization charges can be related by using Poisson’s equation Brotherton (2013):

$E_{interface}=\frac{q\cdot n_{W{O}_x}\cdot x_d}{{ϵ}_0\cdot {ϵ}_{W{O}_x}}(3)$

By combining Eqs 2, 3, the depletion width can be estimated as follows:

$x_d=-\frac{{ϵ}_0\cdot {ϵ}_{WOx}\cdot P_{r-}}{{ϵ}_0\cdot {ϵ}_{HZO}\cdot q\cdot n_{W{O}_x}}=4.5\mathrm{n}\mathrm{m}(4)$

where ${ϵ}_{W{O}_x}$ is the permittivity of WO_x, equal to 189 as measured by (Halter et al., 2020). A more precise determination of x_d is not possible due to the missing ${\mu }_{W{O}_x}$ of the fins, but this approximation confirms that the effect of the polarization on the depletion width is comparable to the channel dimension. The retention properties of FinFeFET have been studied, as shown in Figure 2C. First, the device was set in its LRS by applying a 500 µs pulse of V_write = 5 V. Then, the channel resistance R_SD was monitored and read at 200 mV every few minutes up to 1.5 × 10⁴ s. This was repeated for the HRS using a 500 µs pulse of V_write = −5.5 V, and the evolution of that state was monitored for the same time interval. By fitting a linear regression in the semi-log representation, a drift is observed towards lower values. Extrapolating the fit to 10 years, both the HRS and the LRS are still differentiable, but the dynamic range is reduced. Remarkably, both the states drifted towards lower values, indicating that this drift does not originate from the back-switching of ferroelectric domains caused by the depolarization field, but possibly from an oxygen exchange between the WO_x and HZO, which progressively reduces the channel. This is more pronounced after setting the HRS (oxygen drift from HZO to WO_x by negative write field). The pristine resistance R_SD of each device was extracted. A decreasing trend of R_SD as a function of the number of fins is reported for a representative FinFeFET configuration (Figure 2D). Since multiple-fin configurations are a convolution of the single-fin ones, and since for neuromorphic applications, the absolute resistance should be in the megohm range (Gokmen et al., 2016), further electrical characterization was performed only on single-fin devices, which achieved the targeted resistance and the smallest footprint. The resistive switching of a representative FinFeFET (Figure 2E) with a 10 nm wide and 200 nm long fin, was investigated by applying a DC voltage of varying amplitude V_write on the gate of the memristor, having the source and the drain as common reference, and reading the channel at V_read = 200 mV. A more detailed description of the writing and reading procedures can be found in the Sub-section 4.3. By sweeping V_write from −4 to 4 V, R_SD shows a hysteresis cycle from 1.5 MΩ to 2.4 MΩ (HRS/LRS ≃ 1.53). Set and reset operations occur with a positive and negative programming voltage on the gate, respectively. 30 single fin devices were characterized as previously explained, to measure the static dynamic range (Figure 2F). The variability in the dynamic range may be due to processing at the limit of our lithographic capabilities, as well as an inhomogeneous WO_x material.

FIGURE 2

FIGURE 2. (A) Ohmic conduction in the WO_x channel of FinFeFETs. (B) Positive-Up Negative-Down (PUND) measurements of a MSFM capacitor with an 40 µm² × 40 µm² area. (C) Retention measurements on a single-fin FinFeFET with L_fins = 500 nm and W_fins = 10 nm, at room temperature for 500 µs set/reset pulses. (D) Pristine channel resistance R_SD as a function of the number of fins N_fins. (E) R_SD after the application of a DC-voltage V_write of varying amplitude. Each data point corresponds to a resistance measurement between source and drain at V_read = 200 mV. (F) Dynamic range measured on 30 single-fin FinFeFETs with L_fins = 200 nm and L_fins = 500 nm.

Several pulsing schemes on HZO based FeFETs have been investigated in the past (Jerry et al., 2018). In this work, the scheme using pulses with varying amplitudes and constant width was used since it optimizes the number of accessible polarization states (Jerry et al., 2018). The analog nature of a representative FinFeFET (Figure 3A) having a 10 nm wide and 500 nm long fin, was explored by applying voltage pulses of varying amplitude V_write while keeping a fixed pulse duration of 5 µs. For the potentiation, V_write was increased from 1 to 5 V, and for the depression, decreased from −1 to −5.5 V, with 50 mV steps. A slightly higher voltage was used for the depression to compensate the built-in field in HZO. After each pulse, the channel resistance R_SD was measured at V_read = 200 mV, keeping the gate floating. The memristor showed a HRS of ≃ 1 MΩ and a LRS of ≃ 0.7 MΩ (HRS/LRS ≃ 1.4). With respect to the DC-electrical characterization, almost all the devices had a decrease in dynamic range. This may be explained considering the short programming pulses and that the dynamics of ferroelectric switching in polycrystalline HZO films follow the Merz law (Chanthbouala et al., 2012), (Paruch et al., 2006), hence the coercive field (E_c) depends linearly on the logarithm of the writing time, as detailed for polycrystalline HZO devices in (Bégon-Lours et al., 2021). Another explanation could be an oxygen drift across the HZO—WO_x interface, a much slower process than ferroelectric switching, that would lead to an oxidation/reduction of the WO_x channel. The drift phenomena should be more pronounced in the DC potentiation and depression and thereby lead to the observed dynamic range dependence on the write signal length. The cycle to cycle variability was taken into account averaging all the potentiation and depression cycles (Figure 3B). By decreasing the range of V_write, the dynamic range is reduced. The number of the intermediate states is defined by the potentiation and depression step size, which can be further reduced to increase the resolution. The resistive states are not all differentiable, however the monotonic increasing and decreasing trends are desirable for online learning. The weight-update linearity was quantified by fitting the normalized weight update characteristics, by a function of the normalized pulse number, as proposed by (Chen et al., 2018b; Chen et al., 2018c):

$f\left(x\right)=\frac{1-\exp \left(-\frac{x}{A}\right)}{1-\exp \left(-\frac{1}{A}\right)}(5)$

The parameter A was chosen by minimizing the root mean square error of the fitting. Values of A_LTP = 1.2 for the potentiation and A_LTD = −2.5 for the depression, respectively, were found (see Figure 3C).

FIGURE 3

FIGURE 3. (A) Multiple potentiation and depression cycles of a representative FinFeFET. After each pulse, R_SD is measured. (B) Channel resistance R_SD averaged over 15 potentiation and depression cycles. (C) Experimental data and relative fits using the device behavioral model of the non-linear weight update provided by Chen et al. (2018b) and Chen et al. (2018c). The bottom insert details the pulsing scheme used.

Considering the ferroelectric synaptic weight dependence both on the pulse amplitude and duration, FinFeFETs are promising devices for Spike-Timing-Dependent Plasticity (STDP). However, STDP was not implemented with such devices in this work, since it requires tailored spike shapes as described by (Boyn et al., 2017) in ferroelectric perovskites and by (Max et al., 2020) in ferroelectric hafnia.

3 Conclusion

We developed a manufacturing process to allow the transfer and the scale-down of the FeFET planar technology into a multigate FinFeFET configuration. The fabrication process is compatible with the integration in the back end of line of CMOS technology and is using only abundant materials, making it suitable for large-scale integration. An Ohmic conduction in scaled WO_x fins, as well as good retention, analog states and an almost symmetric and linear potentiation and depression were obtained. Future work will focus on controlling the carrier concentration of WO_x fins, to further increase the resistance range and the dynamic range.

4 Experimental Section

4.1 Sample Fabrication

A 500 nm thick SiO₂ was grown on Si by thermal oxidation. Then, 30 nm WO_x was deposited using a (BuN)₂W(NMe₂)₂ precursor and O₂ plasma at T = 375°C in an Oxford Instruments Plasma-Enhanced Atomic Layer Deposition (PEALD) system. The crystallization and the oxidation of WO_x to WO₃ was performed in a Rapid Thermal Annealer (RTA) by O₂-annealing at T = 350°C for 30min. The WO₃ was then structured using an Inductively Coupled Plasma Reactive Ion Etcher (ICP-RIE) with SF₆ plasma, and Hydrogen Silsesquioxane (HSQ) 2% as negative resist. The source and drain metal contacts were defined by lift-off using a double layer PMMA e-beam resist. 5 nm of W was first deposited by sputtering, then 50 nm of Pt was evaporated prior to the lift-off. An approximately 10 nm thick layer of HZO was grown in PEALD system through a process exploiting alternating cycles of tetrakis-(ethylmethylamino)hafnium (TEMAH) and bis(methyl-η5-cyclopentadienyl)methoxymethylzirconium (ZrCMMM) at T = 300°C. Then, further 10 nm of TiN were deposited using tetrakis-(dimethylamino)titanium (TDMAT) as precursor and N₂/H₂ plasma in a PEALD system. The sample was then immediately transferred to a sputter chamber for the deposition of 40 nm W as gate electrode. Millisecond flash lamp annealing (O’Connor et al., 2018), with a background temperature of 375°C and a flash energy density of 70 J/cm², was performed to crystallize HZO. The patterning of the gate electrode was achieved using a Reactive Ion Etcher (RIE) with SF₆ plasma. Source and drain vias were etched through the HZO by ICP-RIE with CHF₃/O₂ plasma. The passivation consists of 5 nm Al₂O₃ by PEALD using trimethylaluminum (TMA) as precursor and 100 nm SiO₂ by plasma-enhanced chemical vapor deposition (PECVD). Vias were etched using an RIE with a CHF₃/O₂ plasma. Al₂O₃, used as etch stop layer during SiO₂ etching, was then removed by a wet etching in MIF726 developer. Finally, the contacts were realized by depositing 150 nm W by sputtering and defined in a RIE with an SF₆/O₂ plasma.

4.2 Structural Characterization

Grazing-Incidence X-Ray Diffraction (GIXRD) measurements were performed by a Bruker D8 Discover diffractometer equipped with a rotating Cu anode generator. The Scanning Electron Microscope (SEM) system used in this work is the FEI Helios NanoLab 450S.

4.3 Electrical Characterization

The PUND measurements were performed on a TF2000 ferroelectric analyzer from aixACCT with a frequency of 1 kHz on capacitors with an area of 40 µm² × 40 µm². Prior to the PUND measurement, bipolar cycling stress with an AC amplitude of 3.5 V and frequency of 1 kHz for 10³ cycles was applied to wake-up the HZO. The DC and the pulsed electrical characterization of the memristors were performed using an Agilent B1500A semiconductor device analyzer. Before the DC characterization of the dynamic range, a wake-up procedure of the HZO with 100 cycles of ±4 V was applied. Set (reset) of the FinFeFETs was obtained by applying a positive (negative) DC bias of decreasing (increasing) amplitude on the gate, keeping the source and drain electrodes grounded. After the application of each bias (whose duration was not controlled) of amplitude V_write (that varies in the −4 to 4 V range) the channel resistance was measured at V_read = 200 mV, keeping the gate floating. During the pulsed characterization, V_write pulses were generated by a Waveform Generator Fast Measurement Unit (WGFMU) of a Agilent B1500A, and applied directly to the gate through a triax cable, while grounding both the source and the drain. After each pulse, the channel resistance R_SD was measured, keeping the gate floating and applying a voltage sweep from −200 to 200 mV along the channel. R_SD was then determined by reading the resistance at 200 mV.

Data Availability Statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author Contributions

DF has contributed with the fabrication, the electrical measurements and interpretation of the results as well as with the writing of the manuscript. MH initiated the project and contributed with the fabrication, the design and technical guidance, with the interpretation of the data and revision of the manuscript. LB-L and BO have contributed with the interpretation of the data and manuscript revision.

Funding

This work is funded by H2020 FREEMIND (No. 840903), ULPEC (No. 732642), BeFerroSynaptic (No. 871737) and CHIST-ERA, UNICO (No. 20CH21-186952).

Conflict of Interest

DFF, LB-L and BJO were employed by the company IBM Research Europe.

The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors acknowledge the support of the operation team of the Binnig and Rohrer Nanotechnology Center (BRNC), especially Antonis Olziersky for the optimization of the e-beam lithographic processes.

References

Ambriz-Vargas, F., Kolhatkar, G., Broyer, M., Hadj-Youssef, A., Nouar, R., Sarkissian, A., et al. (2017). A Complementary Metal Oxide Semiconductor Process-Compatible Ferroelectric Tunnel junction. ACS Appl. Mater. Inter. 9, 13262–13268. doi:10.1021/acsami.6b16173

PubMed Abstract | CrossRef Full Text | Google Scholar

Baek, I. G., Lee, M. S., Sco, S., Lee, M. J., Seo, D. H., Suh, D.-S., et al. (2004). Highly Scalable Non-volatile Resistive Memory Using Simple Binary Oxide Driven by Asymmetric Unipolar Voltage Pulses. Tech. Dig. - Int. Electron Devices Meet. IEDM. doi:10.1109/iedm.2004.1419228

CrossRef Full Text | Google Scholar

Begon-Lours, L., Halter, M., Popoff, Y., Yu, Z., Falcone, D. F., Davila, D., et al. (2021). Analog Resistive Switching in Beol, Ferroelectric Synaptic Weights. IEEE J. Electron. Devices Soc. 9, 1275–1281. doi:10.1109/JEDS.2021.3108523

CrossRef Full Text | Google Scholar

Bégon-Lours, L., Halter, M., Popoff, Y., Yu, Z., Falcone, D. F., and Offrein, B. J. (2021). “High-Conductance, Ohmic-like HfZrO4 Ferroelectric Memristor,” in ESSDERC 2021 - IEEE 51st European Solid-State Device Research Conference (ESSDERC), 87–90. doi:10.1109/ESSCIRC53450.2021.9567870

CrossRef Full Text | Google Scholar

Bégon-Lours, L., Rouco, V., Qiao, Q., Sander, A., Roldán, M. A., Bernard, R., et al. (2018). Factors Limiting Ferroelectric Field-Effect Doping in Complex Oxide Heterostructures. Phys. Rev. Mater. 2. doi:10.1103/PhysRevMaterials.2.084405

CrossRef Full Text | Google Scholar

Böscke, T. S., Müller, J., Brauhaus, D., Schroder, U., and Bottger, U. (2011). “Ferroelectricity in Hafnium Oxide: Cmos Compatible Ferroelectric Field Effect Transistors,” in Electron Devices Meeting, 1988. IEDM ’88. Technical Digest., International, 24.5.1–24.5.4. doi:10.1109/IEDM.2011.613160699

CrossRef Full Text | Google Scholar

Boybat, I., Le Gallo, M., Nandakumar, S. R., Moraitis, T., Parnell, T., Tuma, T., et al. (2018). Neuromorphic Computing with Multi-Memristive Synapses. Nat. Commun. 9. doi:10.1038/s41467-018-04933-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Boyn, S., Grollier, J., Lecerf, G., Xu, B., Locatelli, N., Fusil, S., et al. (2017). Learning through Ferroelectric Domain Dynamics in Solid-State Synapses. Nat. Commun. 8. doi:10.1038/ncomms14736

PubMed Abstract | CrossRef Full Text | Google Scholar

Brotherton, S. D. (2013). Introduction to Thin Film Transistors. doi:10.1007/978-3-319-00002-2

CrossRef Full Text | Google Scholar

Chanthbouala, A., Garcia, V., Cherifi, R. O., Bouzehouane, K., Fusil, S., Moya, X., et al. (2012). A Ferroelectric Memristor. Nat. Mater 11, 860–864. doi:10.1038/nmat3415

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, L., Wang, T.-Y., Dai, Y.-W., Cha, M.-Y., Zhu, H., Sun, Q.-Q., et al. (2018a). Ultra-low Power hf0.5zr0.5o2 Based Ferroelectric Tunnel junction Synapses for Hardware Neural Network Applications. Nanoscale 10, 15826–15833. doi:10.1039/c8nr04734k

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, P.-Y., Peng, X., and Yu, S. (2017c). “Neurosim+: An Integrated Device-To-Algorithm Framework for Benchmarking Synaptic Devices and Array Architectures,” in International Electron Devices Meeting, IEDM. doi:10.1109/IEDM.2017.8268337

CrossRef Full Text | Google Scholar

Chen, P.-Y., Peng, X., and Yu, S. (2018b). Neurosim: A Circuit-Level Macro Model for Benchmarking Neuro-Inspired Architectures in Online Learning. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 37, 3067–3080. doi:10.1109/TCAD.2018.2789723

CrossRef Full Text | Google Scholar

Cheng, S., Fan, Z., Rao, J., Hong, L., Huang, Q., Tao, R., et al. (2020). Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing. iScience 23, 101874. doi:10.1016/j.isci.2020.101874

PubMed Abstract | CrossRef Full Text | Google Scholar

Colinge, J.-P., Lee, C.-W., Afzalian, A., Akhavan, N. D., Yan, R., Ferain, I., et al. (2010). Nanowire Transistors without Junctions. Nat. Nanotech 5, 225–229. doi:10.1038/nnano.2010.15

PubMed Abstract | CrossRef Full Text | Google Scholar

Colinge, J. P., Kranti, A., Yan, R., Lee, C. W., Ferain, I., Yu, R., et al. (2011). Junctionless Nanowire Transistor (JNT): Properties and Design Guidelines. Solid-State Elect. 65-66, 33–37. doi:10.1016/j.sse.2011.06.004

CrossRef Full Text | Google Scholar

Cui, B., Fan, Z., Li, W., Chen, Y., Dong, S., Tan, Z., et al. (2021). Ferroelectric Photosensor Network: an Advanced Hardware Solution to Real-Time Machine Vision doi:10.21203/rs.3.rs-969097/v1

CrossRef Full Text | Google Scholar

Davis, D. (1973). Thomas-fermi Screening in One Dimension. Phys. Rev. B 7, 129–135. doi:10.1103/PhysRevB.7.129

CrossRef Full Text | Google Scholar

Fuller, E. J., Gabaly, F. E., Léonard, F., Agarwal, S., Plimpton, S. J., Jacobs‐Gedrim, R. B., et al. (2017). Li‐Ion Synaptic Transistor for Low Power Analog Computing. Adv. Mater. 29, 1604310. doi:10.1002/adma.201604310

PubMed Abstract | CrossRef Full Text | Google Scholar

Goh, Y., and Jeon, S. (2018). The Effect of the Bottom Electrode on Ferroelectric Tunnel Junctions Based on Cmos-Compatible Hfo2. Nanotechnology 29, 335201. doi:10.1088/1361-6528/aac6b3

PubMed Abstract | CrossRef Full Text | Google Scholar

Gokmen, T., and Vlasov, Y. (2016). Acceleration of Deep Neural Network Training with Resistive Cross-point Devices: Design Considerations. Front. Neurosci. 10, 333. doi:10.3389/fnins.2016.00333

PubMed Abstract | CrossRef Full Text | Google Scholar

Gokmen, T., Vlasov, Y., and Yurii, (2016). Acceleration of Deep Neural Network Training with Resistive Cross-point Devices: Design Considerations. Front. Neurosci. 10. doi:10.3389/fnins.2016.00333

PubMed Abstract | CrossRef Full Text | Google Scholar

Guo, R., You, L., Zhou, Y., Shiuh Lim, Z., Zou, X., Chen, L., et al. (2013). Non-volatile Memory Based on the Ferroelectric Photovoltaic Effect. Nat. Commun. 4. doi:10.1038/ncomms2990

PubMed Abstract | CrossRef Full Text | Google Scholar

Guo, R., Zhou, Y., Wu, L., Wang, Z., Lim, Z., Yan, X., et al. (2018). Control of Synaptic Plasticity Learning of Ferroelectric Tunnel Memristor by Nanoscale Interface Engineering. ACS Appl. Mater. Inter. 10, 12862–12869. doi:10.1021/acsami.8b01469

PubMed Abstract | CrossRef Full Text | Google Scholar

Halter, M., Bégon-Lours, L., Bragaglia, V., Sousa, M., Offrein, B. J., Abel, S., et al. (2020). Back-end, Cmos-Compatible Ferroelectric Field-Effect Transistor for Synaptic Weights. ACS Appl. Mater. Inter. 12, 17725–17732. doi:10.1021/acsami.0c00877

PubMed Abstract | CrossRef Full Text | Google Scholar

Hyuk Park, M., Joon Kim, H., Jin Kim, Y., Lee, W., Moon, T., and Seong Hwang, C. (2013). Evolution of Phases and Ferroelectric Properties of Thin Hf0.5Zr0.5O2 Films According to the Thickness and Annealing Temperature. Appl. Phys. Lett. 102, 242905. doi:10.1063/1.4811483

CrossRef Full Text | Google Scholar

Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., et al. (2013). Commentary: The Materials Project: A Materials Genome Approach to Accelerating Materials Innovation. APL Mater. 1, 011002. doi:10.1063/1.4812323

CrossRef Full Text | Google Scholar

Jerry, M., Chen, P. Y., Zhang, J., Sharma, P., Ni, K., Yu, S., et al. (2018). “Ferroelectric Fet Analog Synapse for Acceleration of Deep Neural Network Training,” in Technical Digest - International Electron Devices Meeting, IEDM. doi:10.1109/IEDM.2017.8268338

CrossRef Full Text | Google Scholar

Kim, S., Gokmen, T., Lee, H.-M., and Haensch, W. E. (2017). “Analog Cmos-Based Resistive Processing Unit for Deep Neural Network Training,” in Midwest Symposium on Circuits and Systems, 2017-August. doi:10.1109/MWSCAS.2017.8052950

CrossRef Full Text | Google Scholar

Kim, S., Todorov, T., Onen, M., Gokmen, T., Bishop, D., Solomon, P., et al. (2019). “Metal-oxide Based, Cmos-Compatible Ecram for Deep Learning Accelerator,” in Technical Digest - International Electron Devices Meeting, IEDM, 2019-December. doi:10.1109/IEDM19573.2019.8993463

CrossRef Full Text | Google Scholar

Krivokapic, Z., Rana, U., Galatage, R., Razavieh, A., Aziz, A., Liu, J., et al. (2017). “14nm Ferroelectric Finfet Technology with Steep Subthreshold Slope for Ultra Low Power Applications,” in Technical Digest - International Electron Devices Meeting, IEDM. doi:10.1109/IEDM.2017.8268393

CrossRef Full Text | Google Scholar

Lacaita, A. L. (2006). Phase Change Memories: State-Of-The-Art, Challenges and Perspectives. Solid-State Elect. 50, 24–31. doi:10.1016/j.sse.2005.10.046

CrossRef Full Text | Google Scholar

Lawrence, B., and Rubia, J. (2015). “Review of Fin Fet Technology and Circuit Design Challenges,” in Journal of Engineering Research and Applications. www.ijera.com 5.

Google Scholar

Lee, H. Y., Chen, P. S., Wu, T. Y., Chen, Y. S., Wang, C. C., Tzeng, P. J., et al. (2008). “Low Power and High Speed Bipolar Switching with a Thin Reactive Ti Buffer Layer in Robust Hfo2 Based Rram,” in Technical Digest - International Electron Devices Meeting, IEDM. doi:10.1109/IEDM.2008.4796677

CrossRef Full Text | Google Scholar

Li, J., Ge, C., Du, J., Wang, C., Yang, G., and Jin, K. (2020). Reproducible Ultrathin Ferroelectric Domain Switching for High‐Performance Neuromorphic Computing. Adv. Mater. 32, 1905764. doi:10.1002/adma.201905764

PubMed Abstract | CrossRef Full Text | Google Scholar

Majumdar, S., Tan, H., Qin, Q. H., and van Dijken, S. (2019). Energy-efficient Organic Ferroelectric Tunnel junction Memristors for Neuromorphic Computing. Adv. Electron. Mater. 5, 1800795. doi:10.1002/aelm.201800795

CrossRef Full Text | Google Scholar

Materlik, R., Künneth, C., and Kersch, A. (2015a). The Origin of Ferroelectricity in Hf1−xZrxO2: A Computational Investigation and a Surface Energy Model. J. Appl. Phys. 117, 134109. doi:10.1063/1.4916707

CrossRef Full Text | Google Scholar

Materlik, R., Künneth, C., and Kersch, A. (2015b). The Origin of Ferroelectricity in Hf1−xZrxO2: A Computational Investigation and a Surface Energy Model. J. Appl. Phys. 117, 134109. doi:10.1063/1.4916707

CrossRef Full Text | Google Scholar

Max, B., Hoffmann, M., Mulaosmanovic, H., Slesazeck, S., and Mikolajick, T. (2020). Hafnia-based Double-Layer Ferroelectric Tunnel Junctions as Artificial Synapses for Neuromorphic Computing. ACS Appl. Electron. Mater. 2, 4023–4033. doi:10.1021/acsaelm.0c00832

CrossRef Full Text | Google Scholar

Mehta, R. R., Silverman, B. D., and Jacobs, J. T. (1973). Depolarization fields in Thin Ferroelectric Films. J. Appl. Phys. 44, 3379–3385. doi:10.1063/1.1662770

CrossRef Full Text | Google Scholar

Mo, F., Tagawa, Y., Jin, C., Ahn, M., Saraya, T., Hiramoto, T., et al. (2019). “Experimental Demonstration of Ferroelectric Hfo2 Fet with Ultrathin-Body Igzo for High-Density and Low-Power Memory Application,” in Digest of Technical Papers - Symposium on VLSI Technology, 2019-June. doi:10.23919/VLSIT.2019.8776553

CrossRef Full Text | Google Scholar

Moore, G. E. (1965). Cramming More Components onto Integrated Circuits. Electronics 38.

Google Scholar

Mulaosmanovic, H., Chicca, E., Bertele, M., Mikolajick, T., and Slesazeck, S. (2018). Mimicking Biological Neurons with a Nanoscale Ferroelectric Transistor. Nanoscale 10, 21755–21763. doi:10.1039/C8NR07135G

PubMed Abstract | CrossRef Full Text | Google Scholar

Mulaosmanovic, H., Ocker, J., Müller, S., Schroeder, U., Müller, J., Polakowski, P., et al. (2017). Switching Kinetics in Nanoscale Hafnium Oxide Based Ferroelectric Field-Effect Transistors. ACS Appl. Mater. Inter. 9, 3792–3798. doi:10.1021/acsami.6b13866

PubMed Abstract | CrossRef Full Text | Google Scholar

Nishitani, Y., Kaneko, Y., Ueda, M., Morie, T., and Fujii, E. (2012). Three-terminal Ferroelectric Synapse Device with Concurrent Learning Function for Artificial Neural Networks. J. Appl. Phys. 111, 124108. doi:10.1063/1.4729915

CrossRef Full Text | Google Scholar

O’Connor, É., Halter, M., Eltes, F., Sousa, M., Kellock, A., Abel, S., et al. (2018). Stabilization of Ferroelectric HfxZr1−xO2 Films Using a Millisecond Flash Lamp Annealing Technique. APL Mater. 6, 121103. doi:10.1063/1.5060676

CrossRef Full Text | Google Scholar

Paruch, P., Giamarchi, T., Tybell, T., and Triscone, J.-M. (2006). Nanoscale Studies of Domain wall Motion in Epitaxial Ferroelectric Thin Films. J. Appl. Phys. 100, 051608. doi:10.1063/1.2337356

CrossRef Full Text | Google Scholar

Poon, C.-S., and Zhou, K. (2011). Neuromorphic Silicon Neurons and Large-Scale Neural Networks: Challenges and Opportunities. Front. Neurosci. 5. doi:10.3389/fnins.2011.00108

CrossRef Full Text | Google Scholar

Raoux, S., Wełnic, W., and Ielmini, D. (2010). Phase Change Materials and Their Application to Nonvolatile Memories. Chem. Rev. 110, 240–267. doi:10.1021/cr900040x

PubMed Abstract | CrossRef Full Text | Google Scholar

Salje, E., and Güttler, B. (1984). Anderson Transition and Intermediate Polaron Formation in WO3-xTransport Properties and Optical Absorption. Philosophical Mag. B 50, 607–620. doi:10.1080/13642818408238882

CrossRef Full Text | Google Scholar

Sharma, P., Tapily, K., Saha, A. K., Zhang, J., Shaughnessy, A., Aziz, A., et al. (2017). “Impact of Total and Partial Dipole Switching on the Switching Slope of Gate-Last Negative Capacitance Fets with Ferroelectric Hafnium Zirconium Oxide Gate Stack,” in Digest of Technical Papers - Symposium on VLSI Technology. doi:10.23919/VLSIT.2017.7998160

CrossRef Full Text | Google Scholar

Sze, S. M., and Ng, K. K. (2006). Physics of Semiconductor Devices. doi:10.1002/0470068329

CrossRef Full Text | Google Scholar

Tang, J., Bishop, D., Kim, S., Copel, M., Gokmen, T., Todorov, T., et al. (2018). “Ecram as Scalable Synaptic Cell for High-Speed, Low-Power Neuromorphic Computing,” in Technical Digest - International Electron Devices Meeting, 2018-December (IEDM). doi:10.1109/IEDM.2018.8614551

CrossRef Full Text | Google Scholar

Tian, X., and Toriumi, A. (2017). “New Opportunity of Ferroelectric Tunnel junction Memory with Ultrathin Hfo2-Based Oxides,” in 2017 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2017 - Proceedings. doi:10.1109/EDTM.2017.7947507

CrossRef Full Text | Google Scholar

Waser, R., Dittmann, R., Staikov, G., and Szot, K. (2009). Redox-Based Resistive Switching Memories - Nanoionic Mechanisms, Prospects, and Challenges. Adv. Mater. 21, 2632–2663. doi:10.1002/adma.200900375

CrossRef Full Text | Google Scholar

Wong, H.-S. P., and Salahuddin, S. (2015). Memory Leads the Way to Better Computing. Nat. Nanotech 10, 191–194. doi:10.1038/nnano.2015.29

PubMed Abstract | CrossRef Full Text | Google Scholar

Yu, S. (2018). Neuro-inspired Computing with Emerging Nonvolatile Memorys. Proc. IEEE 106, 260–285. doi:10.1109/JPROC.2018.2790840

CrossRef Full Text | Google Scholar

Yu, T., He, F., Zhao, J., Zhou, Z., Chang, J., Chen, J., et al. (2021). Hf0.5zr0.5o2-based Ferroelectric Memristor with Multilevel Storage Potential and Artificial Synaptic Plasticity. Sci. China Mater. 64, 727–738. doi:10.1007/s40843-020-1444-1

CrossRef Full Text | Google Scholar

Zeng, B., Xiao, W., Liao, J., Liu, H., Liao, M., Peng, Q., et al. (2018). Compatibility of Hfn Metal Gate Electrodes with hf0.5zr0.5o2 Ferroelectric Thin Films for Ferroelectric Field-Effect Transistors. IEEE Electron. Device Lett. 39. doi:10.1109/led.2018.2868275

CrossRef Full Text | Google Scholar