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This paper presents the performance of optimal filtering; LMS with a nonvolatile analog memory cell fabricated through 1.2um CMOS process, for the adaptive identification signal problem, where transfer functions are unknown and changing. The memory stores the weight in the filter as charge on the floating gate of a transistor pMOS. The update is linear, using a pulse density modulation scheme by means of tunneling and injection mechanisms. The LMS algorithm is implemented digitally off chip, and it does not require the signal to be piecewise stationary, and requires no manual operation other than selection of the step-size.

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This article presents the optimal performance of a nonvolatile analogue memory cell fabricated in 1.2 mm CMOS process, which is programmed using a LMS (least mean square) algorithm to implement an adaptive FIR filter used to identify an unknown signal. The memory cell is programmed to store and update the weight in the filter as charge in the floating gate of a pMOS transistor (FGMOS). Programming is linear using a pulse density modulation scheme by means of tunnelling and hot injection electrons. The behavior of the memory is included and programming method is developed. The LMS algorithm performed very well, and does not require the signal to be piecewise stationary, and requires no manual operation other than selection of the step-size of the adaptive parameter.