Wan, Qingzhou
(2022)
Iontronic Devices for Neuromorphic Computing and Health Monitoring.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
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Abstract
Iontronics is an emerging interdisciplinary field that bridges electronics and ionics, exploring the electronic properties or functions of the materials controlled by ionic movement and arrangement. Two intriguing mechanisms in iontronics are electrochemical charge doping and electrostatic effects at the electric-double-layer (EDL) interface, which can be leveraged to modulate the carrier density of low-dimensional materials and achieve the supercapacitance at the EDL interface. In this dissertation, we develop two types of iontronic devices: electrochemical synapses and supercapacitive pressure sensors for neuromorphic computing and health monitoring applications, respectively.
We first report three-terminal electrochemical synapses with programmable spatio-temporal dynamics using novel materials such as two-dimensional layered topological insulator (BixSb1-x)2Te3 and perovskite tungsten trioxide. Inspired by the Li-ion battery, the channel conductance (i.e., synaptic weight) of the electrochemical synapses can be continuously and controllably modulated via electrochemical reactions (e.g., involving Li+ ion flows) through a gate terminal. Our electrochemical synapses exhibit a large dynamic range, a high precision (multiple analog states), a linear and symmetric synaptic weight update, and small variations that are ideal for traditional artificial neural networks (ANNs). Additionally, time-dependent synaptic functions such as short-term and long-term plasticity, pair-pulse facilitation, and temporal filtering are demonstrated. The excellent energy efficiency and potential cognitive capabilities of our
electrochemical synapses could lead to the hardware acceleration of brain-inspired, neuro-realistic ANNs.
We also propose a high-fidelity iontronic tonometric sensor (ITS) with high sensitivity (4.82 kPa-1), high linearity (R2 > 0.995), and a large dynamic range (up to 180 % output change) over a broad working range (0-38 kPa) that can fully cover the normal blood pressure (BP) range (5-25 kPa). Our ITS demonstrates a low limit of detection at 40 Pa, a fast load (35 ms) and release time (35 ms), and a stable response over 5000 load/release cycles. We further explore the application of our ITS in monitoring real-time beat-to-beat BP by measuring the brachial and radial pulse waveforms. Our ITS work provides a rational design for a wearable pressure sensor with high sensitivity, high linearity, and a large dynamic range for real-time continuous and non-invasive BP monitoring.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
6 September 2022 |
| Date Type: |
Publication |
| Defense Date: |
22 June 2022 |
| Approval Date: |
6 September 2022 |
| Submission Date: |
24 June 2022 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
143 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Electrical and Computer Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
neuromorphic computing, health monitoring, artificial synapses, iontronic pressure sensors |
| Date Deposited: |
06 Sep 2022 16:17 |
| Last Modified: |
06 Sep 2022 16:17 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/43219 |
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