投身於神經科學(Neuroscience)相關發展的研究者近年來與日俱增，主要原因是其為支持生物工程(Bio-Engineering)進步的關鍵性基本學科。其中，神經訊號的感測及誘發為神經科學發展中一個很重要的主題。目前的神經-電子(Neuro-Electronic)介面技術主要待突破的目標為提高膜外量測(Extracellular)的精密度、選擇比、以及形成高密度的可雙向溝通整合型晶片，以期能夠提供實際神經科學研究所需要的平台。
本篇論文將對一個與CMOS製成相容 (CMOS-compatible)，用於膜外紀錄及誘發一群神經元活動的多電極陣列系統 (Multi-Electrode Array) 做完整的特性分析。 此系統包含前端的二維式氧半電晶體陣列 (2-D OSFET array) 及後端的訊號處理電路。利用晶粒等級的微加工(die-level micromachining) 技術，我們可以在不影響後端電路的前提下，將氧半電晶體結構與電路實現於同一晶片上。論文內將詳述初代晶片的設計過程及考量，並展示包括微加工製程、氧半電晶體電極的電性測試及整體系統的表現。初步生物測試的結果已證明了此微系統應用於紀錄及誘發神經活動的能力。 最後，根據上述的各項測試結果，我們將提出一種更貼近實際神經科學研究需要的二代氧半電晶體陣列晶片設計架構。

The field of neuroscience has drawn gradually expanding attention ever since the middle
of 20th century. This is generally because the desire of human beings to understand
ourselves and that people start to realize certain things are accomplished more efficiently
in the biological world than it is being done today. Many scholars dedicate their study
into bio-inspired, or more specifically, brain-inspired research to further improve our living
environments.
Among all existing apparatuses available for monitoring the response of neuralnetworks,
MicroElectrode Array (MEA) gains the most conspicuous attention because
it allows researchers to communicate with a great quantity of biological signal processing
units (also known as neuron) simultaneously in a harmless way. In this thesis, we try to
identify a MEA system which is fully compatible with current VLSI technology. Under
the consideration of sensitivity, signal integrity and system scalability, a CMOS-compatible
OSFET based MEA chip with integrated circuitry was developed, where OSFET is simply
a MOSFET without its poly gate.
This thesis will first give a quick review in the fabrication method of the prototype
MEA chip. After that comes the complete exams of the 1st generation chip, from electrical
tests (process variation, sensory drift, noise measurement, circuit performance) to
biological tests to verify its functionality. Experimental results confirm the validity of
both recording and stimulating neurons using the chip while several imperfections have
been found during the testing. Based on these collected information, the characters of
using such sensors in monitoring neural activities and its proper back-end circuit topology
will then be concluded. These feedbacks will be discussed in the end of each chapters and
finally, an modified 2nd version OSFET MEA chip is designed and taped out to overcome
existing problems.

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