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Probabilistic modeling and analysis of molecular memory

This article investigates the aspects of designing a nanocell based molecular memory. An empirical model for molecular device is developed, based on circuit behavior of nitro-substituted Oligo (Phynylene Ethynylene) molecule (OPE). This device model is subsequently used to design nanocell based 1-bit memory and verified using HSPICE. The approach is extended to train the nanocell for multibit storage capability using external voltage signals. It is observed that to successfully train a 2-bit molecular memory, the number of control signals should be approx. one-fourth of total number of nanoparticles. A computational framework is proposed to compute the probability of retrieving the stored data bits correctly, at the output terminal of the nanocell buffer. This nanocell configuration is simulated by systematically varying number of nanoparticles and molecular switches. It is observed that the probability of the existence of at least one path from input to output approaches close to unity with presence of 20 or more nanoparticles in a nanocell. During memory model validation, 1000 samples of 1-bit memory (consisting of 20 nanoparticles) were generated and verified for read and write operations. The model verification results obtained for this memory cell closely match those obtained using analytical solution of probabilistic graph model.