Neuron inspired data encoding memristive multi-level memory cell

TL;DR

This paper presents a design and analysis of a data encoder for a memristive multi-level memory cell capable of storing 10 discrete analog voltage levels, inspired by neuron data encoding.

Contribution

It introduces a novel data encoder for controlling memristors in a multi-level memory cell, enabling analog data storage for neuro-inspired hardware applications.

Findings

Successfully designed a data encoder for 10-level memristive memory

Achieved stable programming of memristors for discrete voltage levels

Enhanced potential for analog signal processing in neuromorphic systems

Abstract

Mapping neuro-inspired algorithms to sensor backplanes of on-chip hardware require shifting the signal processing from digital to the analog domain, demanding memory technologies beyond conventional CMOS binary storage units. Using memristors for building analog data storage is one of the promising approaches amongst emerging non-volatile memory technologies. Recently, a memristive multi-level memory (MLM) cell for storing discrete analog values has been developed in which memory system is implemented combining memristors in voltage divider configuration. In given example, the memory cell of 3 sub-cells with a memristor in each was programmed to store ternary bits which overall achieved 10 and 27 discrete voltage levels. However, for further use of proposed memory cell in analog signal processing circuits data encoder is required to generate control voltages for programming memristors to store discrete analog values. In this paper, we present the design and performance analysis of data encoder that generates write pattern signals for 10 level memristive memory.