Ferroelectric artificial synapses for high-performance neuromorphic computing: Status, prospects, and challenges
Le Zhao,
Hong Fang,
Jie Wang
et al.
Abstract:Neuromorphic computing provides alternative hardware architectures with high computational efficiencies and low energy consumption by simulating the working principles of the brain with artificial neurons and synapses as building blocks. This process helps overcome the insurmountable speed barrier and high power consumption from conventional von Neumann computer architectures. Among the emerging neuromorphic electronic devices, ferroelectric-based artificial synapses have attracted extensive interest for their… Show more
“…In addition to the aforementioned insights, optimizing vacuum-based devices and electronic systems using bio-inspired optimization approaches (e.g., Ant Colony Optimization (ACO), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA)) holds considerable promise [66,67]. Since the vacuum gate dielectric degrades the coupling capacitance, leading to worse carrier control, leveraging negative capacitance ferroelectric materials [68][69][70] to improve such carbon nanodevices can be a Simulation of such devices and their derivatives (considering different gate configurations) while taking into account scattering mechanisms within the NEGF framework can provide a deeper insight into the behavior and performance projection of carbon-based nanotransistors in radiative environments. Additionally, exploring the analog/RF performance of these devices presents an intriguing avenue for further investigation.…”
Section: Resultsmentioning
confidence: 99%
“…In addition to the aforementioned insights, optimizing vacuum-based devices and electronic systems using bio-inspired optimization approaches (e.g., Ant Colony Optimization (ACO), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA)) holds considerable promise [ 66 , 67 ]. Since the vacuum gate dielectric degrades the coupling capacitance, leading to worse carrier control, leveraging negative capacitance ferroelectric materials [ 68 , 69 , 70 ] to improve such carbon nanodevices can be a matter for further investigation. Furthermore, very interesting results are presented in [ 71 , 72 , 73 ].…”
This paper investigates the performance of vacuum gate dielectric doping-free carbon nanotube/nanoribbon field-effect transistors (VGD-DL CNT/GNRFETs) via computational analysis employing a quantum simulation approach. The methodology integrates the self-consistent solution of the Poisson solver with the mode space non-equilibrium Green’s function (NEGF) in the ballistic limit. Adopting the vacuum gate dielectric (VGD) paradigm ensures radiation-hardened functionality while avoiding radiation-induced trapped charge mechanisms, while the doping-free paradigm facilitates fabrication flexibility by avoiding the realization of a sharp doping gradient in the nanoscale regime. Electrostatic doping of the nanodevices is achieved via source and drain doping gates. The simulations encompass MOSFET and tunnel FET (TFET) modes. The numerical investigation comprehensively examines potential distribution, transfer characteristics, subthreshold swing, leakage current, on-state current, current ratio, and scaling capability. Results demonstrate the robustness of vacuum nanodevices for high-performance, radiation-hardened switching applications. Furthermore, a proposal for extrinsic enhancement via doping gate voltage adjustment to optimize band diagrams and improve switching performance at ultra-scaled regimes is successfully presented. These findings underscore the potential of vacuum gate dielectric carbon-based nanotransistors for ultrascaled, high-performance, energy-efficient, and radiation-immune nanoelectronics.
“…In addition to the aforementioned insights, optimizing vacuum-based devices and electronic systems using bio-inspired optimization approaches (e.g., Ant Colony Optimization (ACO), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA)) holds considerable promise [66,67]. Since the vacuum gate dielectric degrades the coupling capacitance, leading to worse carrier control, leveraging negative capacitance ferroelectric materials [68][69][70] to improve such carbon nanodevices can be a Simulation of such devices and their derivatives (considering different gate configurations) while taking into account scattering mechanisms within the NEGF framework can provide a deeper insight into the behavior and performance projection of carbon-based nanotransistors in radiative environments. Additionally, exploring the analog/RF performance of these devices presents an intriguing avenue for further investigation.…”
Section: Resultsmentioning
confidence: 99%
“…In addition to the aforementioned insights, optimizing vacuum-based devices and electronic systems using bio-inspired optimization approaches (e.g., Ant Colony Optimization (ACO), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA)) holds considerable promise [ 66 , 67 ]. Since the vacuum gate dielectric degrades the coupling capacitance, leading to worse carrier control, leveraging negative capacitance ferroelectric materials [ 68 , 69 , 70 ] to improve such carbon nanodevices can be a matter for further investigation. Furthermore, very interesting results are presented in [ 71 , 72 , 73 ].…”
This paper investigates the performance of vacuum gate dielectric doping-free carbon nanotube/nanoribbon field-effect transistors (VGD-DL CNT/GNRFETs) via computational analysis employing a quantum simulation approach. The methodology integrates the self-consistent solution of the Poisson solver with the mode space non-equilibrium Green’s function (NEGF) in the ballistic limit. Adopting the vacuum gate dielectric (VGD) paradigm ensures radiation-hardened functionality while avoiding radiation-induced trapped charge mechanisms, while the doping-free paradigm facilitates fabrication flexibility by avoiding the realization of a sharp doping gradient in the nanoscale regime. Electrostatic doping of the nanodevices is achieved via source and drain doping gates. The simulations encompass MOSFET and tunnel FET (TFET) modes. The numerical investigation comprehensively examines potential distribution, transfer characteristics, subthreshold swing, leakage current, on-state current, current ratio, and scaling capability. Results demonstrate the robustness of vacuum nanodevices for high-performance, radiation-hardened switching applications. Furthermore, a proposal for extrinsic enhancement via doping gate voltage adjustment to optimize band diagrams and improve switching performance at ultra-scaled regimes is successfully presented. These findings underscore the potential of vacuum gate dielectric carbon-based nanotransistors for ultrascaled, high-performance, energy-efficient, and radiation-immune nanoelectronics.
“…The design of ternary logic using CNTFET is more prolific than other well technologies due to the ease of adjusting the threshold voltage [54,55]. The CNTFET technology offers ballistic transport [56,57]…”
Section: Literature Survey On Cntfet-based Tsrammentioning
In this paper, a carbon nanotube field-effect transistor (CNTFET) based low power and robust ternary SRAM (TSRAM) cell with enhanced static noise margin (SNM) has been proposed. The proposed cell uses a low-power cell core and a stack of 2 CNTFETs to discharge the read bit line (RBL) to ground, unlike the previous SRAM designs which use read buffers or transmission gates (TG) to alter the voltage levels on the RBL. The proposed TSRAM cell has been simulated relentlessly, using the Stanford 32 nm CNTFET technology mode file with Synopsis HSPICE tool under various operating conditions. Unlike other designs, the cross-coupled ternary inverters used as the cell core in the proposed TSRAM show higher gain and steep curves in the transition region mitigating the static power of the cell. The simulation results exhibit improvements in performance parameters like power consumption, energy, noise margins, and reliability. At 0.9 V supply voltage, the proposed TSRAM cell offers 52.44% and 43.17% reduction in write and read static power, a PDP reduction of 35.29% in comparison, and a 36.36% improvement in SNM compared to best designs under investigation. Also, the proposed TSRAM design shows higher robustness compared to other designs.
“…Among them, ferroelectric memristors consist of a ferroelectric film sandwiched between two electrodes. They offer advantages such as rapid reading and writing speeds, a continuously adjustable resistance state, a substantial switching ratio, and the absence of a need for an electroforming process. − It is well-known that the occurrence of defects such as oxygen vacancies is unavoidable during the deposition of ferroelectric thin films. Therefore, many researchers have turned to the resistive switching (RS) effect of ferroelectric materials for drift and charge trapping in oxygen vacancy defects. − Ferreyra et al revealed the significance of oxygen vacancy migration and polarization direction flipping in the RS and underscored the crucial role played by oxygen vacancies .…”
The ability of ferroelectric memristors to modulate conductance and offer multilevel storage has garnered significant attention in the realm of artificial synapses. On one hand, the resistance change of ferroelectric memristors mainly depends on the polarization reversal. On the other hand, the defects such as oxygen vacancies, which are inevitable presence during hightemperature processes, can undergo diffusion drift with the polarization reversal, thereby change the interface potential barrier. Thus, it is both desirable and necessary to investigate the synergistic effect of ferroelectricity and defects. Here, we prepare BaTiO 3 ferroelectric memristor by pulse laser deposition and achieve resistance switching through the synergistic effect of ferroelectricity and oxygen vacancies. The memristor shows excellent switching characteristics with a large switching ratio (10 4 ) and good stability (10 3 s). It effectively emulates the features of artificial synapses and accomplishes decimal logical neural computing. In the neuromorphic system crafted with the memristor, the recognition accuracy of the 28 × 28 pixel image reaches 94.9%. These findings strongly support the research of ferroelectric memristors in neuromorphic devices.
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