病毒性傳染疾病的診斷和預防一直是每代人類必須面對的課題。抗體快篩技術是一種短時間、低成本、低風險的快速檢測方法，對病毒株變異的容忍度也更大。本文首先重點介紹了應用於生物醫學診斷的光譜晶片光譜儀，並討論其製造過程的光學校正和光譜品質量化解決方案，以提高生產效率和品質控制能力。

光譜晶片(Spectrochip)基於微機電製造技術並作為側向流體免疫層析法快速篩選的讀取器，可提高快篩結果的可靠度，降低錯誤率、樣品用量、篩選時間和醫療人力成本。本論文從免疫學、傳染病學、光學、光刻、製造理論、光譜學、統計學等多個角度解釋了造成光譜品質缺陷的潛在原因。

研究中挑選出了光譜儀系統生產中必不可少的一道工序——波長校正程序，實現其自動化、標準化、資訊化系統的開發，並將品質量化加入該工序之中。品質量化模型建立了基於主成分分析的無標籤品質分類。該模型在保持最大資訊量的前提下對高維光譜晶片品質資料進行降維，然後利用密度聚類演算法完成非監督分類。最終可以從多項指標對光譜晶片品質進行量化和排序，提前檢測出異常。也可以透過可視化聚類結果產生的過程，追溯前端製程與組裝缺陷的變因。

Diagnosis and prevention of viral infectious diseases have always been a topic humans have to face in all generations. Antibody screening technology is a short-time, low-cost, low-risk rapid test method with a greater tolerance for variants. This paper also focuses on the Spectrochip spectrometer for biomedical diagnosis and discusses its manufacturing process's optical calibration and spectral performance quantification solution to improve production efficiency and quality control ability.

Spectrochip bases on MEMS used as a reader for LFIA rapid screening, which can improve the reliability of results and reduce error rate, sample dosage, screening time, and medical labor costs. In this thesis, the potential crises to cause defects in spectral quality from multiple perspectives have been explained, including immunology, epidemiology, optics, lithography, manufacturing theory, spectroscopy, and statistics.

In the research, an essential procedure in spectrometer system production, wavelength calibration, is picked out, and the development of its automated, informationized, standardized system is realized. Moreover, add performance quantification to this process.The performance quantification model builds the label-free quality classification based on principal component analysis. This model performs dimensionality reduction of the high-dimensional Spectrochip performance data under the premise of maintaining the maximum amount of information and then using the density clustering algorithm to complete unsupervised classification.Finally, the method can quantify and rank spectral performance with multi-feature and detect anomalies in advance. By visualizing the process of clustering results, the causes of front-end manufacturing and assembly defects are traceable.

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