第175期 2021年7月刊
 
教師研究成果專欄　│　光電要聞
 
 
發行人：黃建璋所長　　編輯委員：曾雪峰教授　　主編：林筱文　　發行日期：2021.07.30
 
 

Surface Plasmon Resonance-Induced Diffusion-Limited Aggregation in the Formation of Ag/AgOx Nano-Networks as Broad-Spectrum Transparent Conductors

Professor C. C. Yang

Graduate Institute of Photonics and Optoelectronics, National Taiwan University

臺灣大學光電所 楊志忠教授

Based on the generation and transportation of hot electrons and the diffusion-limited aggregation of Ag+ ions induced by the surface plasmon resonance of surface Ag nanoparticles (NPs) with ambient moisture, we develop a new mechanism for fabricating a surface Ag nano-network (NNW) structure on a slightly conductive template through Ag NP reorganization and AgOx formation. Such an extended NNW structure consists of distributed Ag NPs covered and connected by AgOx to form a highly transparent conductive network. Its sheet resistance is as low as ~140 W/square while its transmittance lies between 80 and 90 %. The process of diffusion-limited aggregation is regulated by electrostatic induction between diffuse Ag+ ions in a thin surface water layer condensed from moisture and the sharp tips of an NNW structure during its formation. Oxygen atoms can be dissociated from AgOx when a post thermal treatment at a temperature higher than 412 oC is applied, leaving behind an Ag thin layer, which still connects the remaining Ag NPs to form a different conductive network of ~240 W/square in sheet resistance. Such an NNW structure can stand an elevated temperature up to ~400 oC and is thermally stable. It can extend the transparent spectrum into ultraviolet and near-infrared ranges and can be applied to touch-panel displays, ultraviolet light-emitting diodes, and flexible optoelectronics devices. Figure 1(a) below shows the SEM image of the initial surface Ag NPs before illumination. Figures 1(b)-1(d) show the SEM images of the formed NNW structures with different magnifications after step-5 illumination. In Fig. 1(d), we show the SEM image in the clear area of the NNM structure, in which one can see the remaining low-density, small Ag NPs. Figure 2 shows the transmission spectrum of the sample after each illumination step. We can see that after step-5 illumination, the transmittance is always higher than 83% in the whole visible range.

 Fig. 1 SEM images of an NNW structure.

Fig. 2 Transmission spectrum after each illumination step.

 

Quantitative spectroscopic comparison of the optical properties of mouse cochlea microstructures using optical coherence tomography

Professor Hsiang-Chieh Lee

Graduate Institute of Photonics and Optoelectronics, National Taiwan University

臺灣大學光電所 李翔傑教授

Currently, the cochlear implantation procedure mainly relies on using a hand lens or surgical microscope, where the success rate and surgery time strongly depend on the surgeon’s experience. Therefore, a real-time image guidance tool may facilitate the implantation procedure. In this study, we performed a systematic and quantitative analysis on the optical characterization of ex vivo mouse cochlear samples using two swept-source optical coherence tomography (OCT) systems operating at the 1.06-µm and 1.3-µm wavelengths. The analysis results demonstrated that the 1.06-µm OCT imaging system performed better than the 1.3-µm OCT imaging system in terms of the image contrast between the cochlear conduits and the neighboring cochlear bony wall structure. However, the 1.3-µm OCT imaging system allowed for greater imaging depth of the cochlear samples because of decreased tissue scattering. In addition, we have investigated the feasibility of identifying the electrode of the cochlear implant within the ex vivo cochlear sample with the 1.06-µm OCT imaging. The study results demonstrated the potential of developing an image guidance tool for the cochlea implantation procedure as well as other otorhinolaryngology applications.

Fig. (a) Example cross-sectional OCT image of the mouse cochlea sample treated with ethylenediaminetetraacetic acid (EDTA) for 7 days, acquired with the 1.06 µm OCT system. RM: Reissner’s membrane; SV: scala vestibuli; SM: scala media; ST: scala tympani; OC: organ of Corti. Scale bar: 250 µm. (b) Schematic diagram showing the anatomic features of the mouse cochlea sample. (c) The volumetric rendering of three-dimensional OCT images of the cochlear sample. Red box: bounding box of the rendering.

 

Reference:

Ting-Yen Tsai, Ting-Hao Chen, Hsin-Chien Chen, Chuan-Bor Chueh, Yin-Peng Huang, Yi-Ping Hung, Meng-Tsan Tsai, Bernhard Baumann, Chih-Hung Wang, and Hsiang-Chieh Lee, "Quantitative spectroscopic comparison of the optical properties of mouse cochlea microstructures using optical coherence tomography at 1.06 µm and 1.3 µm wavelengths," Biomed. Opt. Express 12, 2339-2352 (2021)

 
     
 
 

— 資料提供：影像顯示科技知識平台 (DTKP, Display Technology Knowledge Platform) —

— 整理：林晃巖教授、卓真禾 —

競爭式光子神經網路

神經網路（NNs）是受神經啟發的人工智慧概念，它基於非線性元素（神經元）的集體響應來實現計算。透過這種方式，神經網路模仿了生物大腦中計算的最基本面向之一。特定的訊息處理任務通常是在統計優化的基礎上，透過調整網路拓撲來進行“編程”(programming)，亦即是學習。神經網路可以區分狗和貓，或者進行更嚴肅和有用的傑作，並且代表了計算機領域激動人心的最新進展之一。

但是，每個神經元的狀態都需要透過網路的連接進行計算，且其相關的計算量大大地擊敗傳統處理器。與其用數位計算機來模擬神經網路，不如將大量的精力投入到硬體上，其硬體的物理定律模仿特定的神經網路的概念[1]。一個主要的重點是在物理上實現神經網路的連接，而光子處理的平行化則有望實現更快，更高效的神經網路處理器[2]。

在《自然光子學》中，周（Zhou）等人表明，即使使用非專業和現成的元件實現的光子神經網路，也可以勝過專為神經網路應用量身定制的一流圖形處理單元（GPU）[3]。他們所報導的準確性、速度和能源效率，可與競爭式數位神經網路基準測試模型以及現今的GPU媲美，展示了光子解決方案與未來高性能計算的關聯性。

在周（Zhou）等人的工作中，當固態雷射照射，單個神經元的狀態是由數位微鏡裝置（DMD）內部的微鏡的反射來達成。液晶空間光調制器（SLM）上顯示的相位遮罩，對DMD信號的繞射會產生平行和可重新配置的網路連接，透過快速攝影進行光學檢測的模組平方，會為每個神經網路增加非線性。這種串聯的非線性運算會大大地放大數據表示的維數，這最終使神經網路能夠挖掘出隱藏的特徵，然後可以利用它們來進行具有挑戰性的計算。它們執行的互連受到嚴格限制，但是它們通過串聯多層實現了具有競爭式性能。他們計算出他們的繞射處理單元（DPU）達到240.1 TOP s-1，能量效率為1.578 TOP J-1。預算中包括了與運作系統有關的所有組件和設備。值得注意的是，在這兩個指標上，作者均擊敗了輝達（Nvidia）的頂級Tesla V100 Tensor Core GPU。作者所達到的準確性是進一步的實質性進步。讓我們以MNIST數位識別和DPU透過分時多工的三層深神經網路為例。透過物理模型進行的初始模擬可達到97.6％的測試準確度。但是，當將預優化的網路配置轉移到物理DPU時，該值大大降低了。作者繼續通過迭代實驗更新來優化SLM的相位遮罩，直到他們在15個訓練時期後透過實驗獲得96.2％的測試準確度。更複雜的DPU分時多工方案（D-NIN-1（++））（圖1a）實現了捲積神經網絡，在MNIST中具有99％的測試準確度勝過LeNet-4架構的98.9％的準確度（圖1b）。在進一步測試中，例如時尚的MNIST（圖1c）以及人類動作識別，證實了具有競爭式的表現。

圖1、光學神經網路計算。（a）周（Zhou）等人創建了一個主要單元，該單元包括透過繞射處理單元（DPU）並聯連接的光子神經元，以非線性創建特徵空間。空間光調制器使連接可重新配置以進行訓練，並允許一個這樣的原理單元來計算網絡的特徵圖和層的配置，這是深層的網路中的網路（D-NIN-1）拓撲。（b,c）不同的光學神經網路拓撲以性能為基準，例如在標準手寫數字（MNIST）和時尚物品（Fashion-MNIST）數據集之中，周（Zhou）等人的報告推理準確性可與經典電腦上模擬的先前突破性神經網路體系結構（LeNet 1-5）相提並論。

周（Zhou）等人的研究中具有許多含義。DPU利用光子學的平行化可以物理式實現網路連接。最近在具有固定拓撲結構的隨機重複[4]和深層線性網路[5]中顯示，光子方法具有競爭式，並且可以實現GPU優越的縮放性。周（Zhou）等人在一系列通用的拓撲結構中量化此優勢，包括學習並確認光子神經網路可以與GPU上運行的類似神經網路模型競爭，因為它們在當今的一些基準測試數據集之中具有較高的推理精度。此外，該概念是可擴展的：神經元在二維平面中實現，可連接利用沿第三維的光學傳播，而第三維在增加神經元數量時使物理尺度大小線性變化[6]。

我們希望當前對機器學習在光學上的濃厚興趣僅僅是個開始[7]–[9]。最重要的是，實現光學神經網路中的非線性可以使用電子方式，經由電子到光學到電子的轉換來完成。未來的挑戰是以光學方式實現非線性，避免轉換帶來的瓶頸。這有望大大提高性能[10][11]。光領域的原位學習是另一個需要解決的開放性問題，需要在硬體中加以解決和證明。

 

參考資料：

Daniel Brunner & Demetri Psaltis, “Competitive photonic neural networks,” Nature Photonics volume 15, pages323–324(2021)

https://www.nature.com/articles/s41566-021-00803-0

DOI: s41566-021-00803-0
參考文獻：

[1] Marković, D., Mizrahi, A., Querlioz, D. & Grollier, J. Nat.Rev. Phys. 2, 499–510 (2020).

[2] Psaltis, D., Brady, D., Gu, X.-G. & Lin, S. Nature 343, 325–330 (1990).

[3] Zhou, T. et al. Nat. Photon. https://doi.org/10.1038/s41566-021-00796-w (2021).

[4] Rafayelyan, M., Dong, J., Tan, Y., Krzakala, F. & Gigan, S. Phys.Rev. X 10, 41037 (2020).

[5] Lin, X. et al. Science 361, 1004–1008 (2018).

[6] Dinc, N. U., Psaltis, D. & Brunner, D. Photoniques 114, 34–38 (2020).

[7] Wetzstein, G. et al. Nature 588, 39–47 (2020).

[8] Xu, X. et al. Nature 589, 44–51 (2021).

[9] Feldmann, J. et al. Nature 589, 52–58 (2021).

[10] Teğin, U., Yıldırım, M., Oğuz, İ., Moser, C. & Psaltis, D. Preprint at https://arxiv.org/abs/2012.12404 (2020). [11] Porte, X. et al. Preprint at https://arxiv.org/abs/2012.11153 (2020).

 
       
       
 
 
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