The proposed research aims to develop understanding of how stochastic variability will affect circuit design in deep sub-micron processes and to propose novel design methodologies to overcome these intrinsic variations. A novel reconfigurable variability tolerant architecture will be developed and realised both as a simulation model and in hardware, which allows variability aware design and rapid prototyping by exploiting the configuration options of the architecture. These are vital steps towards the next generation of FPGA architectures.
Moore's law states that, since their invention in 1947, every two years the number of transistors on an integrated circuit
doubles. This is due to the shrinking of devices through advances in technology. However, as these devices are
approaching the atomistic level, intrinsic variations are becoming more abundant, leading to a lower production yield and
higher failure rates.
In order to accommodate the increased variability of individual device characteristics there is a need for novel device
architectures and circuit design methodologies. For example, Intel were forced to make the biggest change in transistor
technology since the 1960s in order to reach the 45nm CMOS technology node. These predictions and issues were
originally focussed on large-scale integration, mainly connected with microprocessor design. However, in the last 10
years the rise of Field Programmable devices (e.g. Field Programmable Gate Arrays - FPGA) both in terms of technology
advances and application domains has meant that these issues are now relevant to these devices as well.
Hence, the proposal focuses upon one of the current greatest challenges in electronic design: taking physical effects of
intrinsic variability into account when the shrinking of device sizes approaches atomistic levels, in order to achieve
functional circuit designs. Both process and substrate variations impose major challenges on the reliable fabrication of
such small devices. These variations fall into two categories; deterministic variability, which can be accurately modelled
and accounted for using specific design techniques, and stochastic variability, which can only be modelled statistically
and is harder to overcome.
The proposal will develop a reconfigurable design platform that can be manipulated at the device and digital abstraction
levels in order to further understand and tackle the effects of stochastic variability in hardware upon next generation