conductance states
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Author(s):  
Mohammad Aftab Baig ◽  
Hoang-Hiep Le ◽  
Sourav De ◽  
Che-Wei Chang ◽  
Chia-Chi Hsieh ◽  
...  

Abstract In this paper, multiple-fin n- and p-channel HfZrO2 ferroelectric-FinFET devices are manufactured using a gate first process with post metalization annealing. The device transfer characteristics upon program and erase operations are measured and modeled. The drift in the transfer characteristics due to depolarization field and charge injection are captured using the shift in the threshold voltage along with time-dependent modeling of vertical field dependent mobility degradation parameters to develop a physical, computationally efficient, and accurate retention model for ferroelectric-FinFET devices. The modeled conductance is incorporated into deep neural network simulation platform CIMulator to analyze the role of conductance drift due to retention degradation, as well as the importance of the gap between high and low conductance states in improving the image recognition accuracy of neural networks.


2021 ◽  
Author(s):  
christian nijhuis ◽  
Yulong Wang ◽  
Qian Zhang ◽  
Hippolyte Astier ◽  
Cameron Nickle ◽  
...  

To realize molecular scale electrical operations beyond the von Neumann bottleneck, new types of multi-functional switches are needed that mimic self-learning or neuromorphic computing by dynamically toggling between multiple operations that depend on their past. Here we report a molecule that switches from high to low conductance states with massive negative memristive behavior that depends on the drive speed and the number of past switching events. This dynamic molecular switch emulates synaptic behavior and Pavlovian learning and can provide all of the fundamental logic gates because of its time-domain and voltage-dependent plasticity. This multi-functional switch represents molecular scale hardware operable in solid-state devices opening a pathway to dynamic complex electrical operations encoded within a single ultra-compact component.


2021 ◽  
Author(s):  
Sunwoo Lee ◽  
Jaeyoung Jeon ◽  
Kitae Eom ◽  
Chaehwa Jeong ◽  
Yongsoo Yang ◽  
...  

Abstract Memristors are essential elements for hardware implementation of artificial neural networks. The key functionality of the memristors is to realize multiple non-volatile conductance states with high precision. However, the variation of device conductance limits the number of allowed states. Since actual data for neural network training inherently have a non-uniform distribution, the insufficient number of conductance states and the resultant inaccurate weight quantization may generate significant errors in the memristor-based computation. Herein, we demonstrate a multi-level memristor based on two-dimensional electron gas in a Pt/LaAlO3/SrTiO3 heterostructure. By redistributing oxygen vacancies, we precisely controlled the tunneling conductance of the device, achieving multiple conductance states (more than 27). The multi-level switching capability and the high retention performance allow us to implement a variance-aware weight quantization (VAQ), designed for improved computing accuracy. We verify that the VAQ provides greater accuracy in image classification process, as compared to conventional uniform quantization. These results provide valuable insight into developing high-precision multi-bit memristors for practical neuromorphic processors.


Author(s):  
Zhecheng Guo ◽  
Yuejun Zhang ◽  
Suling Xu ◽  
Zhixin Wu ◽  
Wanlong Zhao

Author(s):  
Shin-young Kang ◽  
Soo-min Jin ◽  
Ju-young Lee ◽  
Dae-seong Woo ◽  
Tae-hun Shim ◽  
...  

Corresponding to the principles of biological synapses, an essential prerequisite for hardware neural networks using electronics devices is continuous regulation of conductance. We implemented artificial synaptic characteristics in a (GeTe/Sb2Te3)16 iPCM with a superlattice structure under optimized identical pulse trains. Based on atomically controlling the Ge switch in the phase transition that appears in the GeTe/Sb2Te3 superlattice structure, multiple conductance states were implemented by applying the appropriate electrical pulses. Furthermore, we found that the bidirectional switching behavior of a (GeTe/Sb2Te3)16 iPCM can achieve a desired resistance level using the pulse width. Therefore, we also fabricated a Ge2Sb2Te5 PCM and designed a pulse scheme based on the phase transition mechanism to compare to the (GeTe/Sb2Te3)16 iPCM. We designed an identical pulse scheme that implements linear and symmetrical LTP and LTD based on the iPCM mechanism. As a result, the (GeTe/Sb2Te3)16 iPCM showed relatively excellent synaptic characteristics by implementing gradual conductance modulation, a nonlinearity value of 0.32, and LTP/LTD 40 conductance states using identical pulses trains. Our results demonstrate the general applicability of the artificial synaptic device for potential use in neuro-inspired computing and next generation non-volatile memory.


2021 ◽  
Vol 15 ◽  
Author(s):  
Yuejun Zhang ◽  
Zhixin Wu ◽  
Shuzhi Liu ◽  
Zhecheng Guo ◽  
Qilai Chen ◽  
...  

The interference of noise will cause the degradation of image quality, which can have a negative impact on the subsequent image processing and visual effect. Although the existing image denoising algorithms are relatively perfect, their computational efficiency is restricted by the performance of the computer, and the computational process consumes a lot of energy. In this paper, we propose a method for image denoising and recognition based on multi-conductance states of memristor devices. By regulating the evolution of Pt/ZnO/Pt memristor wires, 26 continuous conductance states were obtained. The image feature preservation and noise reduction are realized via the mapping between the conductance state and the image pixel. Furthermore, weight quantization of convolutional neural network is realized based on multi-conductance states. The simulation results show the feasibility of CNN for image denoising and recognition based on multi-conductance states. This method has a certain guiding significance for the construction of high-performance image noise reduction hardware system.


2021 ◽  
Author(s):  
Yushan Li ◽  
Wei Cai ◽  
Ruiqiang Tao ◽  
Wentao Shuai ◽  
Jingjing Rao ◽  
...  

Abstract Artificial synapse by inkjet printing is promising in cost-effective and flexible applications, but remains challenging in emulating synaptic dynamics with a sufficient number of stable and effective conductance states under ultra-low voltage spiking operation. Hence, for the first time, a synaptic transistor gated by inkjet-printed hybrid dielectric of electret polyvinyl pyrrolidone (PVP) and high-k Zirconia oxide (ZrOx) is proposed and thus synthesized to solve this issue. Quasi-linear potentiation/depression characteristics with large variation margin of conductance states are obtained through the coupling of these two dielectric components and the facilitating of dipole orientation, which can be attributed to the orderly arranged molecule chains induced by the carefully designed microfluidic flows in droplets. Crucial features of biological synapses including long-term potentiation/depression (LTP/D), spike-timing-dependence-plasticity (STDP) learning rule, “Learning-Experience” behavior, and ultralow energy consumption (< 10 fJ/pulse) are successfully implemented on the device. Simulation results exhibit an excellent image recognition accuracy (97.1 %) after 15 training epochs, which is the highest for printed synaptic transistors. Moreover, the device sustained excellent endurance against bending tests with radius down to 8 mm. This work presents a very viable solution for constructing the futuristic flexible and low-cost neural systems.


Cells ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1737
Author(s):  
Joyce T. Varughese ◽  
Susan K. Buchanan ◽  
Ashley S. Pitt

The voltage-dependent anion channel (VDAC) is a β-barrel membrane protein located in the outer mitochondrial membrane (OMM). VDAC has two conductance states: an open anion selective state, and a closed and slightly cation-selective state. VDAC conductance states play major roles in regulating permeability of ATP/ADP, regulation of calcium homeostasis, calcium flux within ER-mitochondria contact sites, and apoptotic signaling events. Three reported structures of VDAC provide information on the VDAC open state via X-ray crystallography and nuclear magnetic resonance (NMR). Together, these structures provide insight on how VDAC aids metabolite transport. The interaction partners of VDAC, together with the permeability of the pore, affect the molecular pathology of diseases including Parkinson’s disease (PD), Friedreich’s ataxia (FA), lupus, and cancer. To fully address the molecular role of VDAC in disease pathology, major questions must be answered on the structural conformers of VDAC. For example, further information is needed on the structure of the closed state, how binding partners or membrane potential could lead to the open/closed states, the function and mobility of the N-terminal α-helical domain of VDAC, and the physiological role of VDAC oligomers. This review covers our current understanding of the various states of VDAC, VDAC interaction partners, and the roles they play in mitochondrial regulation pertaining to human diseases.


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