In PCA, every principal component a eigen vector?
Yes, a principal component is defined as the eigenvector of the covariance matrix of the data (zero-centered).

The eigenvector with the largest eigenvalue points in the direction of the greatest variance in the data. The smaller the eigenvalue, the less dispersed data points are in the direction of that eigenvector.

I'd recommend reading on the intuition behind PCA on Wikipedia.