Using Machine Learning To Extend Life Of Flash Storage

Flash memory is being drawn into the mainstream of enterprise storage, but its tendency to deteriorate with use remains an Achilles' heel. A paper released at the Aug. 9 start of the Flash Memory Summit in Santa Clara, Calif., finds that machine learning can counteract that deterioration and drastically extend its life cycle.

The paper was written by Tom Coughlin, president of Coughlin Associates (PDF), a solid state consultant in Atascadero, Calif. He is also general chairman of the summit. The paper was sponsored by NVMdurance, a Limerick, Ireland, firm that is applying machine learning in the software it creates for managing solid state devices.

Using machine learning to prolong the useful life of high-capacity SSD systems is a new field.

The fact of that use of flash memory cells results in their physical deterioration as holders of electronic charges (which get translated into digital bits) can't be reversed. However, Coughlin argues that machine learning can understand the pattern of how the solid state device is being used and rejigger registers and voltages to maximize device longevity.

With the complexity and scale of today's SSDs, "the task becomes impossible to do manually," Coughlin wrote in his introduction to the machine learning concept.

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Flash memory works by storing a charge on a floating gate, which can be described as a charge trap. To load the trap, a known voltage level is needed to push electrons through a layer of insulation that allows the cell to hold the charge after the current is taken away.

A key characteristic of flash is that less voltage is needed to load the trap when the memory cell is new. The voltage used may range between 7 and 12 Volts, Coughlin noted. Use of the cell tends to degrade the insulation layer, "making it harder to keep electrons on the floating gate," he continued. Higher voltages are needed as the cell ages, but they result in more degradation of the insulation.

"As electrons leak off the gate over time, this changes the voltage on the floating gate and also leads to bit errors," Coughlin reports. Knowing the rate of leakage becomes a way to predict how long the data in the cell will remain intact. The more frequently the cell is programmed and erased, the weaker the insulation layer becomes, and the life of the device as a whole is gradually shortened.

The process of electrons tunneling out of a charged cell through the insulation and into a neighboring cell is what is known as signal to noise ratio (SNR). The SNR must be kept in check for the flash device to know its data is intact and can be read accurately. Device makers invest heavily in error correction codes that can overcome the noise levels and confirm accurate data is being transferred.

The issue affects NAND devices being widely used today, Coughlin wrote.

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