Joseph Sullivan

A destructive evolutionary algorithm process

This paper describes the application of evolutionary search to the problem of flash memory wear-out. Flash memory differs from standard RAM in that it can wear out due to the manner in which it is programmed. The operating parameters, such as voltage levels, of flash memory are notoriously difficult to determine, as the optimal values vary from batch to batch. The current method in use is an expensive and time-consuming manual process of destructive testing. Understandably, this process is normally undertaken only at design time and testing on individual batches is normally not feasible. The results are sub-optimum solutions which do not minimise wear-out over the lifetime of the device. This is an enormously important issue in manufacturing, as most Flash Memory devices requiring reliability (e.g. solid state device disk drives) often have 100% or more redundancy to compensate for the wear-out rates. We establish the viability of a hardware platform that utilises an Evolutionary Algorithm to perform destructive experimentation on hard silicon in order to discover optimal or, at least favourable, operating parameter settings automatically in a manufacturing environment. Here, we describe this hardware and reveal results demonstrating an average life extension of between 250 and 350% over the factory set conditions with a maximum life extension exhibited of 700% all for cells within the same device over the factory settings. Furthermore, since the process is automated, it is possible to leverage the spread between process batches to further enhance device specifications, facilitating the near no-cost life extension of a split-gate flash memory device.

A Destructive Evolutionary Algorithm Process

This paper describes the application of evolutionary search to the problem of Flash memory wear-out. The current method for establishing memory operating parameters is a time consuming and expensive manual process of destructive testing. Understandably this process is normally undertaken only at design time. The results are sub optimum solutions which do not minimise ware-out over the lifetime of the device. We establish the viability of a hardware platform that utilises an EA (Evolutionary Algorithm) to discover optimal operating parameter settings automatically. Here we describe this hardware and reveal results demonstrating an average life extension of between 250% and 350% over the factory set conditions with a maximum life extension exhibited of 700% for cells within the same device. Furthermore since the process is automated it is possible to leverage the spread between process lots to further enhance device specifications, facilitating the near no cost life extension of a split-gate Flash memory device.