6 research outputs found

    NOISE SHAPING IN SAR ADC

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    The successive approximation register (SAR) analog-to-digital converter (ADC) is currently the most popular type of ADC architecture, owing to its power efficiency. They are also used in multichannel systems, where power efficiency is of high importance because of the large number of simultaneously working channels. However, the SAR ADC architecture is not the most area efficient. In SAR ADCs, the binary weighted capacitive digital-to-analog converter (DAC) is used, which means that one additional bit of resolution costs double the increase of area. Oversampling and noise shaping are methods that allow an increase in resolution without an increase of area. In this paper we present the new SAR ADC architectures with a noise shaping. A first-order noise transfer function (NTF) with zero located nearly at one can be achieved. We propose two modifications of the architecture: with zero-only NTF and with the NTF with additional pole. The additional pole theoretically increases the efficiency of noise shaping to further 3 dB. The architectures were applied to the design of SAR ADCs in a 65 nm complementary metal-oxide semiconductor (CMOS) with OSR equal to 10. A 6-bit capacitive DAC was used. The proposed  architectures  provide nearly 4 additional bits in ENOB. The equalent input bandwitdth is equal to 200 kHz with the sampling rate equal to 4 MS/s

    A 9.35-ENOB, 14.8 fJ/conv.-step Fully-Passive Noise-Shaping SAR ADC

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    Ultra-low Power Circuits and Architectures for Neuromorphic Computing Accelerators with Emerging TFETs and ReRAMs

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    Neuromorphic computing using post-CMOS technologies is gaining increasing popularity due to its promising potential to resolve the power constraints in Von-Neumann machine and its similarity to the operation of the real human brain. To design the ultra-low voltage and ultra-low power analog-to-digital converters (ADCs) for the neuromorphic computing systems, we explore advantages of tunnel field effect transistor (TFET) analog-to-digital converters (ADCs) on energy efficiency and temperature stability. A fully-differential SAR ADC is designed using 20 nm TFET technology with doubled input swing and controlled comparator input common-mode voltage. To further increase the resolution of the ADC, we design an energy efficient 12-bit noise shaping (NS) successive-approximation register (SAR) ADC. The 2nd-order noise shaping architecture with multiple feed-forward paths is adopted and analyzed to optimize system design parameters. By utilizing tunnel field effect transistors (TFETs), the Delta-Sigma SAR is realized under an ultra-low supply voltage VDD with high energy efficiency. The stochastic neuron is a key for event-based probabilistic neural networks. We propose a stochastic neuron using a metal-oxide resistive random-access memory (ReRAM). The ReRAM\u27s conducting filament with built-in stochasticity is used to mimic the neuron\u27s membrane capacitor, which temporally integrates input spikes. A capacitor-less neuron circuit is designed, laid out, and simulated. The output spiking train of the neuron obeys the Poisson distribution. Based on the ReRAM based neuron, we propose a scalable and reconfigurable architecture that exploits the ReRAM-based neurons for deep Spiking Neural Networks (SNNs). In prior publications, neurons were implemented using dedicated analog or digital circuits that are not area and energy efficient. In our work, for the first time, we address the scaling and power bottlenecks of neuromorphic architecture by utilizing a single one-transistor-one-ReRAM (1T1R) cell to emulate the neuron. We show that the ReRAM-based neurons can be integrated within the synaptic crossbar to build extremely dense Process Element (PE)–spiking neural network in memory array–with high throughput. We provide microarchitecture and circuit designs to enable the deep spiking neural network computing in memory with an insignificant area overhead
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