Noise shaping Asynchronous SAR ADC based time to digital converter

Time-to-digital converters (TDCs) are key elements for the digitization of timing information in modern mixed-signal circuits such as digital PLLs, DLLs, ADCs, and on-chip jitter-monitoring circuits. Especially, high-resolution TDCs are increasingly employed in on-chip timing tests, such as jitter and clock skew measurements, as advanced fabrication technologies allow fine on-chip time resolutions. Its main purpose is to quantize the time interval of a pulse signal or the time interval between the rising edges of two clock signals. Similarly to ADCs, the performance of TDCs are also primarily characterized by Resolution, Sampling Rate, FOM, SNDR, Dynamic Range and DNL/INL. This work proposes and demonstrates 2nd order noise shaping Asynchronous SAR ADC based TDC architecture with highest resolution of 0.25 ps among current state of art designs with respect to post-layout simulation results. This circuit is a combination of low power/High Resolution 2nd Order Noise Shaped Asynchronous SAR ADC backend with simple Time to Amplitude converter (TAC) front-end and is implemented in 40nm CMOS technology. Additionally, special emphasis is given on the discussion on various current state of art TDC architectures.Electrical and Computer Engineerin

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.Comment: Added measurements with noise in NEST simulation, add notice about journal publication. Frontiers in Neuromorphic Engineering (2019

A programmable microsystem using system-on-chip for real-time biotelemetry

A telemetry microsystem, including multiple sensors, integrated instrumentation and a wireless interface has been implemented. We have employed a methodology akin to that for System-on-Chip microelectronics to design an integrated circuit instrument containing several "intellectual property" blocks that will enable convenient reuse of modules in future projects. The present system was optimized for low-power and included mixed-signal sensor circuits, a programmable digital system, a feedback clock control loop and RF circuits integrated on a 5 mm × 5 mm silicon chip using a 0.6 μm, 3.3 V CMOS process. Undesirable signal coupling between circuit components has been investigated and current injection into sensitive instrumentation nodes was minimized by careful floor-planning. The chip, the sensors, a magnetic induction-based transmitter and two silver oxide cells were packaged into a 36 mm × 12 mm capsule format. A base station was built in order to retrieve the data from the microsystem in real-time. The base station was designed to be adaptive and timing tolerant since the microsystem design was simplified to reduce power consumption and size. The telemetry system was found to have a packet error rate of 10<sup>-</sup><sup>3</sup> using an asynchronous simplex link. Trials in animal carcasses were carried out to show that the transmitter was as effective as a conventional RF device whilst consuming less power

Phase Locked Loop Test Methodology

Phase locked loops are incorporated into almost every large-scale mixed signal and digital system on chip (SOC). Various types of PLL architectures exist including fully analogue, fully digital, semi-digital, and software based. Currently the most commonly used PLL architecture for SOC environments and chipset applications is the Charge-Pump (CP) semi-digital type. This architecture is commonly used for clock synthesis applications, such as the supply of a high frequency on-chip clock, which is derived from a low frequency board level clock. In addition, CP-PLL architectures are now frequently used for demanding RF (Radio Frequency) synthesis, and data synchronization applications. On chip system blocks that rely on correct PLL operation may include third party IP cores, ADCs, DACs and user defined logic (UDL). Basically, any on-chip function that requires a stable clock will be reliant on correct PLL operation. As a direct consequence it is essential that the PLL function is reliably verified during both the design and debug phase and through production testing. This chapter focuses on test approaches related to embedded CP-PLLs used for the purpose of clock generation for SOC. However, methods discussed will generally apply to CP-PLLs used for other applications

Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor

Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building efficient neural network based architectures for control of fast and agile robots. In this paper, we present a spiking neural network architecture that uses sensory feedback to control rotational velocity of a robotic vehicle. When the velocity reaches the target value, the mapping from the target velocity of the vehicle to the correct motor command, both represented in the spiking neural network on the neuromorphic device, is autonomously stored on the device using on-chip plastic synaptic weights. We validate the controller using a wheel motor of a miniature mobile vehicle and inertia measurement unit as the sensory feedback and demonstrate online learning of a simple 'inverse model' in a two-layer spiking neural network on the neuromorphic chip. The prototype neuromorphic device that features 256 spiking neurons allows us to realise a simple proof of concept architecture for the purely neuromorphic motor control and learning. The architecture can be easily scaled-up if a larger neuromorphic device is available.Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conference

EndoTOFPET-US a Novel Multimodal Tool for Endoscopy and Positron Emission Tomography

The EndoTOFPET-US project aims to jointly exploit Time-Of-Flight Positron Emission Tomography (TOFPET) and ultrasound endoscopy with a multi-modal instrument for the development of new biomarkers for pancreas and prostate oncology. The paper outlines the functionality of the proposed instrument and the challenges for its realization. The high level of miniaturization and integration poses strong demands to the fields of scintillating crystallography, ultra-fast photon detection, highly integrated electronics and system integration. Solutions are presented to obtain a coincidence time resolution better than 200 ps and a spatial resolution of ~1 mm with an asymmetric TOFPET detector. A tracking system with better than 1 mm spatial resolution precision enables the online alignment of the system. The detector design, the production and test status of the single detecto

A 16-channel Digital TDC Chip with internal buffering and selective readout for the DIRC Cherenkov counter of the BABAR experiment

A 16-channel digital TDC chip has been built for the DIRC Cherenkov counter of the BaBar experiment at the SLAC B-factory (Stanford, USA). The binning is 0.5 ns, the conversion time 32 ns and the full-scale 32 mus. The data driven architecture integrates channel buffering and selective readout of data falling within a programmable time window. The time measuring scale is constantly locked to the phase of the (external) clock. The linearity is better than 80 ps rms. The dead time loss is less than 0.1% for incoherent random input at a rate of 100 khz on each channel. At such a rate the power dissipation is less than 100 mw. The die size is 36 mm2.Comment: Latex, 18 pages, 13 figures (14 .eps files), submitted to NIM

A VHDL-AMS Simulation Environment for an UWB Impulse Radio Transceiver

Ultra-Wide-Band (UWB) communication based on the impulse radio paradigm is becoming increasingly popular. According to the IEEE 802.15 WPAN Low Rate Alternative PHY Task Group 4a, UWB will play a major role in localization applications, due to the high time resolution of UWB signals which allow accurate indirect measurements of distance between transceivers. Key for the successful implementation of UWB transceivers is the level of integration that will be reached, for which a simulation environment that helps take appropriate design decisions is crucial. Owing to this motivation, in this paper we propose a multiresolution UWB simulation environment based on the VHDL-AMS hardware description language, along with a proper methodology which helps tackle the complexity of designing a mixed-signal UWB System-on-Chip. We applied the methodology and used the simulation environment for the specification and design of an UWB transceiver based on the energy detection principle. As a by-product, simulation results show the effectiveness of UWB in the so-called ranging application, that is the accurate evaluation of the distance between a couple of transceivers using the two-way-ranging metho

Dynamic Power Management for Neuromorphic Many-Core Systems

This work presents a dynamic power management architecture for neuromorphic many core systems such as SpiNNaker. A fast dynamic voltage and frequency scaling (DVFS) technique is presented which allows the processing elements (PE) to change their supply voltage and clock frequency individually and autonomously within less than 100 ns. This is employed by the neuromorphic simulation software flow, which defines the performance level (PL) of the PE based on the actual workload within each simulation cycle. A test chip in 28 nm SLP CMOS technology has been implemented. It includes 4 PEs which can be scaled from 0.7 V to 1.0 V with frequencies from 125 MHz to 500 MHz at three distinct PLs. By measurement of three neuromorphic benchmarks it is shown that the total PE power consumption can be reduced by 75%, with 80% baseline power reduction and a 50% reduction of energy per neuron and synapse computation, all while maintaining temporary peak system performance to achieve biological real-time operation of the system. A numerical model of this power management model is derived which allows DVFS architecture exploration for neuromorphics. The proposed technique is to be used for the second generation SpiNNaker neuromorphic many core system