太赫茲波因擁有穿透非光學不透明的材質，並且對生物組織相對無害等特殊 特性，有諸多研究指出，太赫茲是生物醫學影像中的斷層掃描、半導體電路檢測、物質分析等應用中的有效工具，因此在近年來，太赫茲研究快速蓬勃的發展。
儘管太赫茲強大的特性已在許多研究中證實，但是太赫茲技術用於成像時會 面臨太赫茲輻射源與接受器成本昂貴的問題，並且，目前也無高敏感度的偵測器 陣列，因此一種同時可兼顧高成像品質、高成像速度的技術是單像素壓縮感知成 像系統。單像素成像僅利用單個接收器，透過更換多個空間遮蔽來同時採樣和壓 縮訊號，最後再利用壓縮感知理論與可靠的還原演算法進行訊號重建。
本研究基於太赫茲時域光譜單像素壓縮感知成像系統進行物質分析的探討，提出一個可實際應用的估測模型，透過太赫茲穿透物質還原之三維張量訊號，導出物質折射率與物質厚度對各回波的時間延遲方程式，進而計算出物質折射係數與物質厚度，增加其應用層面，最終建出物質分析估測系統。同時，本研究亦導入子區塊採樣技巧於此系統中以減少運算量，在本研究之系統規格上可減少89%運算量。
除此之外，本研究為了加速系統內的張量訊號重建過程，設計出一硬體處理器，考慮到晶片面積成本的限制，本研究基於現存之張量廣義軟臨界值還原演算法，提出一適用於硬體架構的張量旋轉矩陣化廣義軟臨界值還原演算法。提出的 演算法可將晶片內部儲存張量的面積縮減為儲存矩陣的面積，並且透過張量尋轉、交換等張量運算性質，與直接將現存演算法張量矩陣化的方式相比，可進一步的 降低53%的資料存取次數與20%運算量。最終，本研究之處理器以台積電28奈米製程下線，繞線後時脈為257 MHz、晶片面積為1.001平方毫米、吞吐量為一秒鐘2.73個32×32×32×32元素的結果、重建所需時間為366毫秒。

Terahertz waves exhibit unique characteristics, such as the ability to penetrate optically opaque materials and being relatively harmless to biological tissues. Numerous studies have identified terahertz waves as an effective tool in applications such as biomedical imaging for tomography, semiconductor circuit inspection, and material analysis. Consequently, terahertz research has experienced rapid growth in recent years.

Despite the proven powerful properties of terahertz waves, their application in imaging faces challenges due to the high cost of terahertz sources and detectors. Additionally, there are currently no high-sensitivity detector arrays available. A technique that can achieve both high imaging quality and speed is the single-pixel compressive sensing imaging system. Single-pixel imaging uses only one detector to sample and compress signals simultaneously by employing multiple spatial masks, then reconstructing signals through compressive sensing theory and reliable reconstruction algorithms.

This research based on the THz-TDS (Time-domain spectroscopy) single-pixel compressive sensing imaging system to explore material analysis, proposes a practical estimation model and derives time-delay equations for estimating material refractive index and thickness with a 3-D tensor signal reconstructed from the complete transient response of terahertz waves penetrating the material. Moreover, a sub-block sampling technique is introduced into the estimation system to reduce the computational complexity for the existing reconstruction algorithm - T-GIST, achieving an 89% reduction in computation based on the system specifications used in this study.

In addition, to accelerate the tensor signal reconstruction process, this study proposes a 3-D tensor reconstruction processor. Considering the area cost constraints of the chip, this study also proposes a tensor unfolding with permutation generalized iterative soft-thresholding (TUP-GIST) reconstruction algorithm suitable for hardware architecture, building on the existing hardware-friendly T-GIST algorithm. The proposed algorithm reduces the SRAM size for storing tensors within the chip to that needed for storing matrices. With tensor operations such as permutation and commutative law, the proposed algorithm further reduces data access by 53% and operation number by 20% when compared to the direct tensor unfolding of the existing algorithm. Finally, the processor is implemented using the TSMC 28 nm process, achieving a post-layout clock speed of 257 MHz, a chip area of 1.01 square millimeters, a throughput of 2.73 results per second for a 32×32×32×32 tensor, and the reconstruction time of 366 milliseconds.

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