We present a VLSI design methodology to address the cross-talk problem, which is becoming increasingly important in Deep Sub-Micron (DSM) IC design. In our approach, we implement the logic netlist in the form of a network of medium sized PLAs. We utilize two regular layout "fabrics" in our methodology, one for areas where PLA logic is implemented, and another for routing regions between such logic blocks. We show that a single PLA implemented in the first fabric style is not only cross-talk immune, but also about 2× smaller and faster than a traditional standard cell based implementation of the same logic. The second fabric, utilized in the routing region between individual PLAs, is also highly cross-talk immune. Additionally, in this fabric, power and ground signals are essentially "pre-routed" all over the die.Our synthesis flow involves decomposing the design into a network of PLAs, each of which has a bounded width and height. The number of inputs and outputs of each PLA are flexible as long as the resulting PLA width is bounded. We perform folding of PLAs to achieve better logic density. Routing is performed using 2, 3, 4, 5 and 6 routing layers. State-of-the-art commercial routing tools are utilized for the experiments involving the use of 3, 4, 5 and 6 routing layers.We have implemented the entire design flow using these ideas. Our scheme results in a reduction in the cross-talk between signal wires of between one and two orders of magnitude. As a result, for a 0.1μm process, the delay variation due to cross-talk dramatically drops from 2.47:1 to 1.02:1. Additionally, our methodology results in circuits that are extremely fast and dense, with a timing improvement of about 15% and an overall area penalty of about 3% compared to standard cells. The regular arrangement of metal conductors in our scheme results in low and highly predictable inductive and capacitive parasitics, resulting in highly predictable designs. The crosstalk immunity, high speed, low area overhead and high predictability of our methodology indicate that it is a strong candidate as the preferred design methodology in the DSM era.