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Organic Electrochemical Transistors ...

Perez, Jake Camron.

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Organic Electrochemical Transistors for Use in Neuromorphic Computing: Boolean and Reversible Logic Gate Solutions.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Organic Electrochemical Transistors for Use in Neuromorphic Computing: Boolean and Reversible Logic Gate Solutions./
Author:	Perez, Jake Camron.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	75 p.
Notes:	Source: Masters Abstracts International, Volume: 82-01.
Contained By:	Masters Abstracts International82-01.
Subject:	Electrical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27960965
ISBN:	9798607333492

Organic Electrochemical Transistors for Use in Neuromorphic Computing: Boolean and Reversible Logic Gate Solutions.
Perez, Jake Camron.

Organic Electrochemical Transistors for Use in Neuromorphic Computing: Boolean and Reversible Logic Gate Solutions. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 75 p.

Source: Masters Abstracts International, Volume: 82-01.

Thesis (M.S.)--University of Colorado at Boulder, 2020.

This item must not be sold to any third party vendors.

This work shows the design and simulation of organic neuron neuromorphic circuits (ONNs) being used to emulate Boolean and reversible logic gates by using multi-gate organic electrochemical transistors (OECTs) as artificial neurons. These circuits are called organic neuron neuromorphics (ONNs) and the Boolean ONNs consist of an input and hidden layer with widths of two OECTs, and an output layer with a single OECT. The multi-gate OECTs sum the voltages of the prior layer, allowing multiple neuron connections between layers to be made without extra circuitry, and is connected to resistors that turn the output current behavior into a voltage input for the next layer. These resistors also act as weights; however, the weighting is complex, depending on the OECT's inputs, resulting in a variant artificial neural network. The simulated Boolean ONNs are low power, for example the XOR dissipates 2.22 nJ, when operating at 333Hz. Since ONNs are limited by OECT switching speeds and the XOR ONN was not optimized for power consumption, as OECT switching speed increases and ONNs are power optimized, they will become incredibly energy efficient.The Boolean ONNs were connected to construct a full adder circuit, which cascades in a combination of parallel and series geometries. Cascading in series is feasible for a handful of ONNs, as shown in the full adder circuit, because the ONN's have been designed to have input buffers; however, beyond a handful of series ONNs, optimization may need to be performed to ensure correct internal voltages to maintain correct operation. Cascading in parallel does not have this issue. The Boolean ONNs were connected to produce the first OECT based Toffoli and double Feynman (DFG) reversible logic gates implementations. A new method to fabricate these devices is discussed, called PCB stacking, and some preliminary experimental results are shown. PCB stacking is a method to fabricate multi-gate OECTs, between layers of stacked PCBs. PCB stacking limits the maximum layer width to 3; however, depth is not affected. Future work would consist of designing and fabricating larger scale ONNs that solve more complex, not exclusively digital, problems such as decoders and ADCs.

ISBN: 9798607333492Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Analog

Organic Electrochemical Transistors for Use in Neuromorphic Computing: Boolean and Reversible Logic Gate Solutions.
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520 $a This work shows the design and simulation of organic neuron neuromorphic circuits (ONNs) being used to emulate Boolean and reversible logic gates by using multi-gate organic electrochemical transistors (OECTs) as artificial neurons. These circuits are called organic neuron neuromorphics (ONNs) and the Boolean ONNs consist of an input and hidden layer with widths of two OECTs, and an output layer with a single OECT. The multi-gate OECTs sum the voltages of the prior layer, allowing multiple neuron connections between layers to be made without extra circuitry, and is connected to resistors that turn the output current behavior into a voltage input for the next layer. These resistors also act as weights; however, the weighting is complex, depending on the OECT's inputs, resulting in a variant artificial neural network. The simulated Boolean ONNs are low power, for example the XOR dissipates 2.22 nJ, when operating at 333Hz. Since ONNs are limited by OECT switching speeds and the XOR ONN was not optimized for power consumption, as OECT switching speed increases and ONNs are power optimized, they will become incredibly energy efficient.The Boolean ONNs were connected to construct a full adder circuit, which cascades in a combination of parallel and series geometries. Cascading in series is feasible for a handful of ONNs, as shown in the full adder circuit, because the ONN's have been designed to have input buffers; however, beyond a handful of series ONNs, optimization may need to be performed to ensure correct internal voltages to maintain correct operation. Cascading in parallel does not have this issue. The Boolean ONNs were connected to produce the first OECT based Toffoli and double Feynman (DFG) reversible logic gates implementations. A new method to fabricate these devices is discussed, called PCB stacking, and some preliminary experimental results are shown. PCB stacking is a method to fabricate multi-gate OECTs, between layers of stacked PCBs. PCB stacking limits the maximum layer width to 3; however, depth is not affected. Future work would consist of designing and fabricating larger scale ONNs that solve more complex, not exclusively digital, problems such as decoders and ADCs.
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