A metalayer (ML) composed of indium antimonide, ferroelectric crystals, and porous silicon (Psi) with high transmission, sensing properties, wide detecting range, and multiple physical measurements is proposed which is optimized by the improved ant colony algorithm (IACO). Due to the quasi-phase matching principle and nonlinear optical properties, the second harmonic waves (SHWs) with high conversion efficiency are successfully generated in the micrometer operating band and form a high and narrow transmission peak for sensing detection. Through analysis using the transfer matrix method (TMM) which is driven by IACO to accelerate the optimization of structures, the sensing performance sensitivity (S), quality factor (Q), figure of merit (FOM), and detection limit (DL) are calculated. The proposed ML demonstrates the capability to measure thickness ( $S=18.75$ , $Q=11\,\,885$ , FOM = $332\,\,\mu \text{m}^{-1}$ ), external temperature ( $S=4.6$ nm/K, $Q=7046$ , FOM = 0.049 K−1), and magnetic field intensity ( $S=1332$ nm/T, $Q=8146$ , FOM = 16.3 T−1). Additionally, it exhibits an ultrawide refractive index (RI) sensing range from 1 to 5, and excels in detecting hydrobacteria, and viruses with good sensing performance ( $S=44\,\,000$ and 43 460 nm/RIU), high Q values (5585 and 6103), and low DL ( $1.38 \times 10^{-4}$ and $1.3 \times 10^{-4}$ RIU). This versatile and promising ML design offers possibilities for comprehensive multiphysical and chemical parameter measurements in diverse fields.

中文翻译：

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基于铁电晶体二次谐波的多孔硅腔的宽范围多物理化学测量的理论研究

提出了一种由锑化铟、铁电晶体和多孔硅（Psi）组成的具有高透射率、传感特性、宽检测范围和多种物理测量的元层（ML），并通过改进蚁群算法（IACO）进行优化。由于准相位匹配原理和非线性光学特性，在微米工作频段成功产生了高转换效率的二次谐波（SHW），并形成高而窄的传输峰用于传感检测。通过使用IACO驱动的传输矩阵法（TMM）加速结构优化进行分析，得到传感性能灵敏度（S）、品质因数（Q）、品质因数（FOM）和检测限（DL）计算出来的。所提出的 ML 展示了测量厚度的能力（ $S=18.75$ , $Q=11\,\,885$ , FOM = $332\,\,\mu \text{m}^{-1}$ ）、外部温度（ $S=4.6$纳米/K, $Q=7046$ ，FOM = 0.049 K−1)，以及磁场强度 ( $S=1332$纳米/吨， $Q=8146$ ，FOM = 16.3 T−1)。此外，它还具有 1 至 5 的超宽折射率 (RI) 传感范围，擅长检测水细菌和病毒，具有良好的传感性能（ $S=44\,\,000$和 43 460 nm/RIU）、高 Q 值（5585 和 6103）和低 DL（ $1.38 \乘以10^{-4}$和 $1.3 \乘以10^{-4}$RIU）。这种多功能且有前景的机器学习设计为不同领域的全面多物理和化学参数测量提供了可能性。