非破壞性摻雜濃度表徵對於半導體製程控制至關重要。本研究利用時間相關二次諧波產生（TD-SHG）技術來評估厚度約 10 nm 的矽超薄膜中的摻雜濃度。透過分析內部光電發射誘導的電荷捕獲以及相應的電場調制，我們建立了一個模型，結合了費米-狄拉克分布與穿隧機率，能夠在不考慮晶體結構的前提下，準確確定 1017 to 1020 atoms/cm³ 範圍內的摻雜濃度。相較於傳統的電性量測與二次離子質譜（SIMS）分析，TD-SHG 提供了一種非破壞性、高效率且適用於在線檢測的替代方案，適用於半導體製造過程中的摻雜監測。
此外，我們進一步探討硼摻雜與環境條件對電場動態的影響。研究發現氧分子會透過奪取表面態電子來降低 SHG 強度，從而影響測得的電場。通過在受控壓力條件下進行實驗，我們成功減少了氧氣影響，使摻雜效應得以獨立呈現。實驗結果顯示，硼誘導的電場與摻雜濃度之間具有穩定的單調關係，並與第一性原理計算和電容-電壓（C-V）測量結果高度一致。此外，雷射功率與時間常數的相關性進一步驗證了多光子吸收機制，增強了 TD-SHG 作為摻雜半導體材料表徵技術的靈敏度。
我們還展示了 SHG 在提取界面陷阱密度（Dit）方面的能力，通過引入物理信息神經網絡（PINN）與轉移學習，大幅降低了計算時間，同時保持高準確性。相比於傳統基因演算法需要約 30 分鐘處理每組數據，PINN 技術能夠將計算時間縮短至 10 秒以內，使得 Dit 的即時評估成為可能，提供了一種高效且非破壞性的半導體界面表徵方法，進一步提升元件製造過程的優化能力。
除了半導體應用外，SHG 亦被用於研究二硫化鎢（WS2）表面氣體吸附行為，揭示氧氣、氨氣與水蒸氣之間的競爭相互作用。結果表明，氣體吸附行為符合朗繆爾吸附模型與波茲曼分布，證實了物理吸附特性。此外，利用 SHG 及簡化鍵超極化率模型（SBHM），我們為研究氣體吸附及其對二維材料電子特性的影響奠定了基礎。進一步透過密度泛函理論計算分析不同氣體間的競爭行為，提供對氣體感測機制與表面相互作用的深入理解。這些研究結果顯示，SHG 作為一種多功能表徵技術，不僅適用於半導體與表面界面研究，亦可為在線監測、材料研究與氣體感測應用開闢新途徑。

Non-destructive characterization of dopant concentration is essential for process control in semiconductor fabrication. In this study, we utilize time-dependent second harmonic generation (TD-SHG) to evaluate dopant concentration in silicon ultrathin films with thicknesses around 10 nm. By analyzing the evolution of internal photoemission-induced charge trapping and the associated electric field modulation, we develop a model incorporating Fermi-Dirac distribution and tunneling probability. This enables the precise determination of dopant concentrations ranging from 1017 to 1020 atoms/cm³ without requiring crystallinity assumptions. Compared to conventional electrical and SIMS measurements, TD-SHG offers a non-destructive, efficient, and in-line alternative for dopant monitoring in semiconductor fabrication.
Furthermore, we investigate the role of boron doping and environmental conditions on electric field dynamics. Oxygen molecules are found to reduce SHG intensity by depleting electrons from surface states, thereby affecting the measured field. By conducting experiments under controlled pressure conditions, we mitigate the influence of oxygen and isolate dopant-related effects. A robust monotonic correlation between boron-induced electric fields and dopant concentration is observed, aligning well with first-principles calculations and capacitance-voltage measurements. The laser power dependence of time constants further validates the multiphoton absorption mechanism, reinforcing TD-SHG’s capability as a sensitive characterization tool for doped semiconductor materials.
Additionally, we demonstrate SHG’s capability in extracting interface trap density (Dit) by employing a physics-informed neural network (PINN) with transfer learning, significantly reducing computational time while maintaining accuracy. This method provides a non-invasive and efficient approach for semiconductor interface characterization, enhancing process optimization in device fabrication. The genetic algorithm approaches required approximately 30 minutes per dataset, whereas PINN reduces this to under 10 seconds, making real-time Dit evaluation feasible.
Beyond semiconductor applications, SHG is employed to examine gas adsorption on WS2, revealing competitive interactions between oxygen, ammonia, and water vapor. The findings align with Langmuir adsorption and Boltzmann distribution models, confirming physical adsorption behavior. By leveraging SHG and the simplified bond hyperpolarizability model, we establish a foundation for studying gas adsorption and its impact on electronic properties in 2D materials. The competition between different gases is further analyzed using density functional theory calculations, providing insights into gas-sensing mechanisms and surface interactions. These results underscore SHG as a versatile tool for both semiconductor and surface interface characterization, paving the way for advanced in-line monitoring, material research, and gas sensing applications.

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