Ring-Based Resonant Standing Wave Oscillators for 3D Clocking Applications

Abstract

Ring-based resonant standing wave oscillators have been shown to be a useful clocking tech-nique that can distribute and generate a high frequency, low skew, low power, and stable clock signal. By using through-silicon-vias, this type of standing wave oscillator can be used to gener-ate the clocking scheme for 3D integrated circuits. In this thesis, we propose the use of such 3D standing wave oscillators and show how independent 3D oscillators in different stacks can syn-chronize through the use of a redistribution layer stub. Inter-chip clock synchronization is then accomplished without the need for a PLL. In addition, we propose the first 3D ring-based resonant standing wave oscillator bootstrap and reset circuit to initialize and stop oscillation. Using a 3D ring-based resonant standing wave oscillator, we propose a ring-based data fabric for 3D stacked DRAM and compare the results with existing approaches such as High Bandwidth Memory (HBM) or Wide I/O memory. We show that our Memory Architecture using a Ring-based Scheme (MARS) can provide the increases in speed necessary to overcome current memory bottlenecks, and can scale effectively as future 3D stacks become larger. Our MARS can trade off power, throughput, and latency to match different application requirements. By using a narrow bus, and connecting it to all channels, the MARS8 can provide an alternative memory configuration with ∼ 6.9× lower power consumption than HBM, and ∼ 2.7× faster speeds than Wide I/O. Using multiple ring topologies in the same stack, the channel count can double from 8 to 16, and then to 32. This is possible since MARS uses about 4× fewer TSVs per channel than HBM or Wide I/O. This provides speeds up to ∼ 4.2× faster than traditional HBM. This scalable architecture allows higher throughput and faster system performance for next-generation DRAM. The MARS topology proposed in this thesis can be used in a variety of computing systems, from lightweight IoT to large-scale data centers

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