Brian Hoskins

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Using memristive properties common for titanium dioxide thin film devices, we designed a simple write algorithm to tune device conductance at a specific bias point to 1% relative accuracy (which is roughly equivalent to seven-bit precision) within its dynamic range even in the presence of large variations in switching behavior. The high precision state is(More)
Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex, with its approximately 10(14) synapses, makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally challenging. To provide comparable complexity while operating much faster and with(More)
Metal-oxide memristors have emerged as promising candidates for hardware implementation of artificial synapses - the key components of high-performance, analog neuromorphic networks - due to their excellent scaling prospects. Since some advanced cognitive tasks require spiking neuromorphic networks, which explicitly model individual neural pulses ("spikes")(More)
Oxide-based resistive switching devices are promising candidates for new memory and computing technologies. Poor understanding of the defect-based mechanisms that give rise to resistive switching is a major impediment for engineering reliable and reproducible devices. Here we identify an unintentional interface layer as the origin of resistive switching in(More)
The paper presents experimental demonstration of 6-bit digital-to-analog (DAC) and 4-bit analog-to-digital conversion (ADC) operations implemented with a hybrid circuit consisting of Pt/TiO2-x/Pt resistive switching devices (also known as ReRAMs or memristors) and a Si operational amplifier (opamp). In particular, a binary-weighted implementation is(More)
Monolithic three-dimensional integration of memory and logic circuits could dramatically improve performance and energy efficiency of computing systems. Some conventional and emerging memories are suitable for vertical integration, including highly scalable metaloxide resistive switching devices (“memristors”), yet integration of logic circuits proves to be(More)
The purpose of this work was to demonstrate the feasibility of building recurrent artificial neural networks with hybrid complementary metal oxide semiconductor (CMOS)/memristor circuits. To do so, we modeled a Hopfield network implementing an analog-to-digital converter (ADC) with up to 8 bits of precision. Major shortcomings affecting the ADC's precision,(More)