**Table 1.**


**Figure 7.**

*Proposed Dual Core PCF with Perfectly Matched Layer (PML) Boundary.*

**Figure 8.** *Experimental Setup to Obtain Outcomes.*

arranged. With the help of this setup the proposed dual core PCF can be utilized with AI to serve better and improved outcomes. The above mentioned **Figure 3 (c)** represents the infected cell by using designed PCF structure integrated with AI. Relative sensitivity and confinement loss is also displayed in **Figure 3 (d & e)**.

In this setup the optical source is used to supply power to the Fiber. By using the splicing technique fiber can be connected with the proposed PCF. IN and OUT ports are used to control the unknown analytes whose refractive Index (RI) need to be identified. When analyte interacts then the variations in terms of lows and peaks occurs which can be observed and displayed using computer. The outcomes so obtained can be enhanced to provide efficient result with AI. Dual core silica PCF

*Photonics for AI and AI for Photonics: Material and Characteristics Integration DOI: http://dx.doi.org/10.5772/intechopen.97781*


**Table 2.**

*Test Performed for various parameters.*

serve as a sensing element used to sense the selected parameter and the AI technology boost the effects of results so obtained.

With the Above mentioned proposed design the following tested have been performed using Dual core Silica PCF.

Depending upon the refractive index of blood serum, the intensity of light is modulated and detected at other end of PCF [16]. The relation between evanescent field absorbed by sensing species and intensity modulation at output end is observed.

Sensitivity is obtained by using

$$\mathbf{r}\_f = f\left(\frac{n\_r}{n\_c}\right) \tag{1}$$

Where nr is the refractive index of the fluid, nc is core refractive index, rf is relative sensitivity coefficient and 'f' is the ratio of optical power with in large holes to the total power which is given as

$$f = \int \left[ \left( E\_{\rm x} H\_{\rm y} - H\_{\rm x} E\_{\rm y} \right)\_{sample} / \int \left[ \left( E\_{\rm x} H\_{\rm y} - H\_{\rm x} E\_{\rm y} \right) \right]\_{total} \tag{2}$$

Confinement loss [17, 18] is calculated by

$$\mathbf{L\_{C}} = \begin{pmatrix} 40\pi/\ln\ \,(10)\,\lambda \end{pmatrix} \,\mathrm{Im}\,(\mathbf{n}\,\mathbf{f}\mathbf{f})\,\mathrm{[dB/km]}\tag{3}$$

or it can be written as

$$\mathbf{L}\_{\text{C}} \left( \text{dB/m} \right) = \mathbf{8.686} \ k\_{\text{0}} \ \mathbf{l}\_{\text{m}} \left( \mathbf{n}\_{\text{eff}} \right) \times \mathbf{10}^{\text{6}} \tag{4}$$

Here n*eff* signifies imaginary part of effective refractive index, and k0 is the free-space number.

The data set of blood serum, ethanol and water for this case of investigation is selected as an input which can be passed through the setup and results so obtained have been optimized by using AI. These results obtained numerically and experimentally have been presented in above mentioned **Table 2**.

#### **5. Discussion**

The potency of the AI standards lies in its capacity to deal with anonymous computing troubles. It is practically identified that it is giving not only innovative or optimized solutions and forecasting, but also original substantial impending to the structure by using integration with technologies. Here we have presented an integrated discussion between AI and Photonics. The AI has been utilized to nurture tiny investigational datasets in iterative method to envisage new materials and

execute multi objective optimization of properties for selected materials. Correspondingly Photonics is also offering new materials for booming realization and performing computation takes in an efficient manner to AI. The characteristics of the dual-core photonic crystal fiber (PCF) sensor are studied using the finite element method (FEM), and the structure is improved according to the numerical simulation results.

## **6. Conclusions**

In the revolutionary field of optics and photonics, most of the work has so far been offered on purpose of photonics to the realization of AI to the intend, expansion and optimization of photonic meta-materials and various devices. AI techniques present prospects both to expand physical approaching and to investigate constraints in a more proficient manner.

Most successful paradigms of AI and photonics like Neuro-morphic electronic system, Optical Neural Network (ONN), Nano Photonics, meta-materials, optical sensing, optical imaging have also been demonstrated here in this chapter in which AI is boosting photonics and similarly photonics is also helping AI to perform efficiently. The proposed Dual core Silica PCF is used to identify infected cell in a human body. Due to easily presence of Silica glass and its vibrant characteristics it is preferred for the proposed PCF design. The refractive index of selected material is 1.458, Specific heat capacity is 720 J/Kg-K, Light Transmission wavelength is 0.18– 2.5micrometer. It has been observed that the relative sensitivity for ethanol, blood serum and water is 56.90%, 46.51% and 53.57% respectively. Similarly the confinement loss for the proposed structure is 2.37 <sup>10</sup><sup>6</sup> , 3.814 <sup>10</sup><sup>10</sup> and 8.063 <sup>10</sup><sup>11</sup> dB/km respectively for the same parameters as mentioned above.

### **Acknowledgements**

I would like to acknowledge each and everyone those who have helped me directly or indirectly to complete this research article. I tried to cite all the resources at my best, but if i forgot someone then kindly receive my apologies in advance.

## **Conflict of interest**

I declare no conflict of interest for this research article.

### **Data availability**

The data that support the findings of this study are available from the corresponding author upon reasonable request.

*Photonics for AI and AI for Photonics: Material and Characteristics Integration DOI: http://dx.doi.org/10.5772/intechopen.97781*
