Dissertation/Thesis Abstract

Design and Automation for High Fidelity Flexible Hybrid Electronics
by Shao, Leilai, Ph.D., University of California, Santa Barbara, 2020, 147; 28089018
Abstract (Summary)

Design and Automation for High Fidelity Flexible Hybrid Electronics

by Leilai Shao

Flexible electronics is emerging as an alternative to conventional silicon electronics for appli- cations such as wearable sensors, artificial skin, medical patches, bendable displays, foldable solar cells and disposable RFID tags. Combining FE with thinned silicon chips, known as flexi- ble hybrid electronics (FHE), can take advantages of both low-cost printed electronics and high performance silicon chips. There exist several challenges before FHE can be broadly employed for next-generation wearable and IoT products. Due to material properties, TFTs are usually mono-type, either only p- or only n-type, devices. Existing CMOS design methodologies for silicon electronics, therefore, cannot be directly applied for designing flexible electronics. To address these challenges, a trustworthy TFT compact model and process design kit (PDK) is needed to facilitate simulations and design explorations.

In the first part, we developed the compact model for thin film transistors, which has been validated extensively with carbon nanotube (CNT), organic and indium gallium oxide (IGZO) devices. The developed model has been implemented in Verilog-A, which can perform co- simulations with silicon chips. With the developed model, we further built the FHE-PDK for flexible thin-film transistors (TFTs) and passive elements, including technology files for design rule checking (DRC), layout versus schematic (LVS) and layout parasitics extraction (LPE), as well as SPICE-compatible models. Wafer scale measurements are used to validate our SPICE models and design rules are derived accordingly to assure a satisfactory yield. With the developed FHE-PDK, we further built the robust Pseudo-CMOS cell library to address the mono-type design challenges.

In the second part, we focused on addressing FHE system design issues. Specifically, motion noises in the flex-rigid interface and sensor defects in large area sensing system. We proposed the ”active electrode” (with a thickness ≤2 um), which integrates the electrode with a thin-film transistor (TFT) based amplifier, to effectively suppress motion artifacts. The fab- ricated ultra-thin amplifier can achieve a gain of 32 dB at 20 kHz. The simulation results indicate that the active electrode can significantly improve the signal quality under motion noise (achieving ≥30 dB improvement in signal-to-noise ratio (SNR)) and boost classification accuracy by ≥19% for atrial fibrillation (AF) detection. We further study robustness issue of ultra-thin flexible electronics caused by inadequate device yield, reliability and stability which is inevitable due to the low temperature requirement for fabrication and the large-area nature of flexible sensing arrays. As signals sensed by body sensor arrays exhibit sparse statistical char- acteristics, we present a system design solution to leverage the sparse nature via compressed sensing (CS) which can ensure system robustness without relying on highly reliable devices. Specifically, we implement a flexible CS encoder together with the sensor array using carbon- nanotube-based flexible TFTs and decode the compressed signal in the silicon side. Our quan- titative analysis, validated through two case studies: temperature imaging and tactile-sensor based object recognition, showed that the proposed robust sensing schema can accommodate up to 20% sparse defects (device defects or transient errors).

Indexing (document details)
Advisor: Cheng, Kwang-Ting , Xie, Yuan
Commitee: Huang, Tsung-Ching , Zhang, Zheng
School: University of California, Santa Barbara
Department: Electrical & Computer Engineering
School Location: United States -- California
Source: DAI-B 82/4(E), Dissertation Abstracts International
Subjects: Computer Engineering, Electrical engineering
Keywords: Flexible hybrid electronics, IoT, Wearable
Publication Number: 28089018
ISBN: 9798684681912
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