Spiers Memorial Lecture: Molecular mechanics and molecular electronics

We describe our research into building integrated molecular electronics circuitry for a diverse set of functions, and with a focus on the fundamental scientific issues that surround this project. In particular, we discuss experiments aimed at understanding the function of bistable [2]rotaxane molecular electronic switches by correlating the switching kinetics and ground state thermodynamic properties of those switches in various environments, ranging from the solution phase to a Langmuir monolayer of the switching molecules sandwiched between two electrodes. We discuss various devices, low bit-density memory circuits, and ultra-high density memory circuits that utilize the electrochemical switching characteristics of these molecules in conjunction with novel patterning methods. We also discuss interconnect schemes that are capable of bridging the micrometre to submicrometre length scales of conventional patterning approaches to the near-molecular length scales of the ultra-dense memory circuits. Finally, we discuss some of the challenges associated with fabricated ultra-dense molecular electronic integrated circuits

Learning Behavior of Memristor-Based Neuromorphic Circuits in the Presence of Radiation

In this paper, a feed-forward spiking neural network with memristive synapses is designed to learn a spatio-temporal pattern representing the 25-pixel character ‘B’ by separating correlated and uncorrelated afferents. The network uses spike-timing-dependent plasticity (STDP) learning behavior, which is implemented using biphasic neuron spikes. A TiO2 memristor non-linear drift model is used to simulate synaptic behavior in the neuromorphic circuit. The network uses a many-to-one topology with 25 pre-synaptic neurons (afferent) each connected to a memristive synapse and one post-synaptic neuron. The memristor model is modified to include the experimentally observed effect of state-altering radiation. During the learning process, irradiation of the memristors alters their conductance state, and the effect on circuit learning behavior is determined. Radiation is observed to generally increase the synaptic weight of the memristive devices, making the network connections more conductive and less stable. However, the network appears to relearn the pattern when radiation ceases but does take longer to resolve the correlation and pattern. Network recovery time is proportional to flux, intensity, and duration of the radiation. Further, at lower but continuous radiation exposure, (flux 1x1010 cm−2 s−1 and below), the circuit resolves the pattern successfully for up to 100 s

A solid-state switch containing an electrochemically switchable bistable poly[n]rotaxane

Electrochemically switchable bistable main-chain poly[n]rotaxanes have been synthesised using a threading-followed-by-stoppering approach and were incorporated into solid-state, molecular switch tunnel junction devices. In contrast to single-station poly[n]rotaxanes of similar structure, the bistable polymers do not fold into compact conformations held together by donor–acceptor interactions between alternating stacked p-electron rich and p-electron deficient aromatic systems. Films of the poly[n]rotaxane were incorporated into the devices by spin-coating, and their thickness was easily controlled. The switching functionality was characterised both (1) in solution by cyclic voltammetry and (2) in devices containing either two metal electrodes or one metal and one silicon electrode. Devices with one silicon electrode displayed hysteretic responses with applied voltage, allowing the devices to be switched between two conductance states, whereas devices containing two metal electrodes did not exhibit switching behaviour. The electrochemically switchable bistable poly[n]rotaxanes offer significant advantages in synthetic efficiency and ease of device fabrication as compared to bistable small-molecule [2]rotaxanes

Two-Dimensional Molecular Electronics Circuits

Addressing an array of bistable [2]rotaxanes through a two‐dimensional crossbar arrangement provides the device element of a current‐driven molecular electronic circuit. The development of the [2]rotaxane switches through an iterative, evolutionary process is described. The arrangement reported here allows both memory and logic functions to use the same elements

A 160-kilobit molecular electronic memory patterned at 10^(11) bits per square centimetre

The primary metric for gauging progress in the various semiconductor integrated circuit technologies is the spacing, or pitch, between the most closely spaced wires within a dynamic random access memory (DRAM) circuit. Modern DRAM circuits have 140nm pitch wires and a memory cell size of 0.0408 μm^2. Improving integrated circuit technology will require that these dimensions decrease over time. However, at present a large fraction of the patterning and materials requirements that we expect to need for the construction of new integrated circuit technologies in 2013 have ‘no known solution’. Promising ingredients for advances in integrated circuit technology are nanowires, molecular electronics and defect-tolerant architectures, as demonstrated by reports of single devices and small circuits. Methods of extending these approaches to large-scale, high-density circuitry are largely undeveloped. Here we describe a 160,000-bit molecular electronic memory circuit, fabricated at a density of 10^(11) bits cm^(-2) (pitch 33 nm; memory cell size 0.0011 mm^2), that is, roughly analogous to the dimensions of a DRAM circuit projected to be available by 2020. A monolayer of bistable, [2]rotaxane molecules 10 served as the data storage elements. Although the circuit has large numbers of defects, those defects could be readily identified through electronic testing and isolated using software coding. The working bits were then configured to form a fully functional random access memory circuit for storing and retrieving information

Modeling Memristor Radiation Interaction Events and the Effect on Neuromorphic Learning Circuits

An ideal memristor model is modified to include the effects of radiation interactions with the device. Modeling is done in Cadence Virtuoso design suite using Verilog-A. Simulations include the effect of radiation events that could change the state of device or can ionize the device to create e−h+ pairs or change the off-state resistance of the device. Combination of these events occurring simultaneously is also studied. Simulation results are compared with the experimental results published in existing research papers. Finally, transient simulation of a three-input, two-output spiking electronic neural network with memristive synapses is performed. Varying amounts of energy deposited by radiation are modeled, and it is observed that radiation exposure dramatically alters the synaptic weight evolution