Generally speaking, if you train for a very large number of epochs, and if your network has enough capacity, the network will overfit. So, to ensure overfitting: pick a network with a very high capacity, and then train for many many epochs. Don't use regularization (e.g., dropout, weight decay, etc.).
Experiments have shown that if you train for long enough, networks can memorize all of the inputs in the training set and achieve 100% accuracy, but this doesn't imply it'll be accurate on a validation set. One of the primary ways we avoid overfitting in most work today is by early stopping: we stop SGD after a limited number of epochs. So, if you avoid stopping early, and use a large enough network, you should have no problem causing the network to overfit.
Do you want to really force lots of overfitting? Then add additional samples to the training set, with randomly chosen labels. Now choose a really large network, and train for a long time, long enough to get 100% accuracy on the training set. The extra randomly-labelled samples is likely to further impede any generalization and cause the network to perform even worse on the validation set.