SPICE simulation of nanoscale non-crystalline silicon TFTs in spiking neuron circuits

Cantley, Kurtis D.; Subramaniam, Anand Bala; Stiegler, H.; Chapman, R. A.; Vogel, Eric M.

doi:10.1109/mwscas.2010.5548881

Cited by 12 publications

(3 citation statements)

References 24 publications

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“…Moreover, the maximum frequency of a biological neuron may reach several hundred hertz (i.e. milliseconds) [50], whereas the SET model designed in Ref. [21] cannot emulate this situation well.…”

Section: Parameter Settings Of the Primary Nanoscale Devicesmentioning

confidence: 99%

Memristive-synapse spiking neural networks based on single-electron transistors

Long

Zhang

2019

J Comput Electron

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In recent decades, with the rapid development of artificial intelligence technologies and bionic engineering, the spiking neural network (SNN), inspired by biological neural systems, has become one of the most promising research topics, enjoying numerous applications in various fields. Due to its complex structure, the simplification of SNN circuits requires serious consideration, along with their power consumption and space occupation. In this regard, the use of SSN circuits based on single-electron transistors (SETs) and modified memristor synapses is proposed herein. A prominent feature of SETs is Coulomb oscillation, which has characteristics similar to the pulses produced by spiking neurons. Here, a novel window function is used in the memristor model to improve the linearity of the memristor and solve the boundary and terminal lock problems. In addition, we modify the memristor synapse to achieve better weight control. Finally, to test the SNN constructed with SETs and memristor synapses, an associative memory learning process, including memory construction, loss, reconstruction, and change, is implemented in the circuit using the PSPICE simulator.

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Section: Parameter Settings Of the Primary Nanoscale Devicesmentioning

confidence: 99%

Memristive-synapse spiking neural networks based on single-electron transistors

Long

Zhang

2019

J Comput Electron

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“…1a, which is a heavily modified version of that originally proposed by Mead [17]. The TFT and memristor models have been discussed extensively in previous work [18,19] in which each synapse consisted of one TFT and one memristor. Here, the synapse has been simplified to a single memristor whose state variable w/D (definition provided in [1]) represents the synaptic weight.…”

Section: Device Modelsmentioning

confidence: 99%

Spike timing-dependent synaptic plasticity using memristors and nano-crystalline silicon TFT memories

Cantley

Subramaniam

Stiegler

et al. 2011

2011 11th IEEE International Conference on Nanotechnology

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Neural circuits based on ambipolar nanocrystalline silicon TFTs and memristive synapses are investigated via SPICE simulations. The drive transistor for the memristive devices is an ambipolar TFT with memory that could be physically implemented using a metal nanoparticle layer within the gate dielectric. It is shown that using such a device adds spike-timing dependence to changes in the synaptic weight. In experiments with action potential pairs, the synaptic weight modification is similar to biological data. Further, asymmetric temporal integration of the weight change is demonstrated using pre-post-pre and post-pre-post spike triplets. Finally, the dependence of weight changes on frequency is presented. This is followed by a discussion of applications and issues which require further analysis.

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“…Fabrication of nanoscale TFTs has been demonstrated using electron beam lithography (EBL) [9]. Submicron device dimensions are essential for use in high-density architectures such as neuromorphic systems [17]. In addition, nc-Si TFTs can be fabricated at temperatures of 250 ∘ C and below, allowing the incorporation of alternative substrates including polyimide [18] and steel foil [19].…”

Section: Introductionmentioning

confidence: 99%

Logic Gates and Ring Oscillators Based on Ambipolar Nanocrystalline-Silicon TFTs

Subramaniam

Cantley

Vogel

2013

Active and Passive Electronic Components

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Nanocrystalline silicon (nc-Si) thin film transistors (TFTs) are well suited for circuit applications that require moderate device performance and low-temperature CMOS-compatible processing below 250°C. Basic logic gate circuits fabricated using ambipolar nc-Si TFTs alone are presented and shown to operate with correct outputs at frequencies of up to 100 kHz. Ring oscillators consisting of nc-Si TFT-based inverters are also shown to operate at above 20 kHz with a supply voltage of 5 V, corresponding to a propagation delay of <10 μs/stage. These are the fastest circuits formed out of nanocrystalline silicon TFTs to date. The effect of bias stress degradation of TFTs on oscillation frequency is also explored, and relatively stable operation is shown with supply voltages >5 V for several hours.

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SPICE simulation of nanoscale non-crystalline silicon TFTs in spiking neuron circuits

Cited by 12 publications

References 24 publications

Memristive-synapse spiking neural networks based on single-electron transistors

Memristive-synapse spiking neural networks based on single-electron transistors

Spike timing-dependent synaptic plasticity using memristors and nano-crystalline silicon TFT memories

Logic Gates and Ring Oscillators Based on Ambipolar Nanocrystalline-Silicon TFTs

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