A Floating Gate Memory with U-Shape Recessed Channel for Neuromorphic Computing and MCU Applications

Gan, Lurong; Wang, Yarong; Chen, Lin; Zhu, Hao; Sun, Qizhen

doi:10.3390/mi10090558

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…The brain's ability to perform in‐memory processing within a unified ionic mechanism has driven many researchers to apply ion‐driven non‐volatile memories to emulate learning rules at the device‐level. [ 1–12 ]…”

Section: Figurementioning

confidence: 99%

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Lin

Chen

et al. 2020

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The biological brain has set a golden standard in computational efficiency, both due to its massive parallelism, and its ability to perform in-memory computing within the same ionic substrate. Ion-gated channels determine synaptic strength which is known to be a mechanism for memory storage, and the very same ions that pass through these gates encode data in the form of spikes. Communication, computation, and storage all occur within the same local medium. The brain's ability to perform in-memory processing within a unified ionic mechanism has driven many researchers to apply ion-driven non-volatile memories to emulate learning rules at the device-level. [1-12] The operating principles of resistive random access memory (RRAM) draw parallel with biological synapses. From a physical standpoint, the top electrode (TE) corresponds to a pre-synaptic terminal, the insulator layer acts as the substrate through which neurotransmitters are released, and the bottom electrode (BE) Biologically plausible computing systems require fine-grain tuning of analog synaptic characteristics. In this study, lithium-doped silicate resistive random access memory with a titanium nitride (TiN) electrode mimicking biological synapses is demonstrated. Biological plausibility of this RRAM device is thought to occur due to the low ionization energy of lithium ions, which enables controllable forming and filamentary retraction spontaneously or under an applied voltage. The TiN electrode can effectively store lithium ions, a principle widely adopted from battery construction, and allows state-dependent decay to be reliably achieved. As a result, this device offers multi-bit functionality and synaptic plasticity for simulating various strengths in neuronal connections. Both short-term memory and long-term memory are emulated across dynamical timescales. Spike-timing-dependent plasticity and paired-pulse facilitation are also demonstrated. These mechanisms are capable of self-pruning to generate efficient neural networks. Time-dependent resistance decay is observed for different conductance values, which mimics both biological and artificial memory pruning and conforms to the trend of the biological brain that prunes weak synaptic connections. By faithfully emulating learning rules that exist in human's higher cortical areas from STDP to synaptic pruning, the device has the capacity to drive forward the development of highly efficient neuromorphic computing systems.

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Section: Figurementioning

confidence: 99%

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Lin

Chen

et al. 2020

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“…1.0, the top gate is wrapped around the bottom MFIS stack such that a maximized electrostatic coupling is present at the bottom stack. In essence, this configuration aims for enhanced interfacial oxide fields as commonly applied in the floating gate memory concept [18]. As AR-TB decreases to unity, the MFMFIS is patterned at once to produce equal electrostatic coupling between the top and bottom stack structures.…”

Section: The Mfmfis Fefet P-e and Fet Characteristicsmentioning

confidence: 99%

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

et al. 2021

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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 current I D(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.

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“…Compared with conventional silicon-based memory, memory devices with emerging memory technologies, including RRAM, PCM, FeRAM, and STT-MRAM, demonstrate less power consumption. In addition, these emerging memory devices show stronger resistance against the research trend of device miniaturization [94,95], which is applied to help data centers deal with their ever-increasing power requirement.…”

Section: Power Consumptionmentioning

confidence: 99%

Memristive Non-Volatile Memory Based on Graphene Materials

Shen

Zhao

et al. 2020

Micromachines

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Resistive random access memory (RRAM), which is considered as one of the most promising next-generation non-volatile memory (NVM) devices and a representative of memristor technologies, demonstrated great potential in acting as an artificial synapse in the industry of neuromorphic systems and artificial intelligence (AI), due its advantages such as fast operation speed, low power consumption, and high device density. Graphene and related materials (GRMs), especially graphene oxide (GO), acting as active materials for RRAM devices, are considered as a promising alternative to other materials including metal oxides and perovskite materials. Herein, an overview of GRM-based RRAM devices is provided, with discussion about the properties of GRMs, main operation mechanisms for resistive switching (RS) behavior, figure of merit (FoM) summary, and prospect extension of GRM-based RRAM devices. With excellent physical and chemical advantages like intrinsic Young’s modulus (1.0 TPa), good tensile strength (130 GPa), excellent carrier mobility (2.0 × 105 cm2∙V−1∙s−1), and high thermal (5000 Wm−1∙K−1) and superior electrical conductivity (1.0 × 106 S∙m−1), GRMs can act as electrodes and resistive switching media in RRAM devices. In addition, the GRM-based interface between electrode and dielectric can have an effect on atomic diffusion limitation in dielectric and surface effect suppression. Immense amounts of concrete research indicate that GRMs might play a significant role in promoting the large-scale commercialization possibility of RRAM devices.

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A Floating Gate Memory with U-Shape Recessed Channel for Neuromorphic Computing and MCU Applications

Cited by 4 publications

References 21 publications

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

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

Memristive Non-Volatile Memory Based on Graphene Materials

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