Graphene Genetics

Abstract

As advances in silicone CMOS technology steadily plateau, new avenues of electronics design must be explored. Graphene Nanoribbons (GNRs) are a potential solution. Current GNR designs require wasteful exhaustive searches for the required device topologies, limiting the size of the solution space that can be searched. This is fine for simple circuits, but more complex functionality requires a different approach. This work presents a new way of identifying suitable GNR device topologies for any functionality. It also presents an example of this, demonstrating how a circuit using the resultant GNR devices can outperform conventional, more complex circuits.

The proposed methodology uses an evolutionary algorithm to efficiently search a large solution space of possible GNR device topologies. This is done while only having to simulate the behavior of a minuscule fraction of the device topologies in this solution space. The GNR devices found by this evolutionary algorithm are used to implement a 4-bit Analog-to-Digital Converter (ADC), where each bit of the circuit consists of only a couple of GNR devices, in contrast to the many more transistors required for conventional designs.

The resulting ADC circuit performs better than conventional ADC designs in terms of energy cost, conversion delay, and required circuit area by several orders of magnitude.