The design and evaluation of the imaging system Clear-PEM for positron emission mammography, under development by the PEM Consortium within the framework of the Crystal Clear Collaboration at CERN, is presented. The proposed apparatus is based on fast, segmented, high atomic number radiation sensors with depth-of-interaction measurement capabilities, and state-of-the-art data acquisition techniques. The camera consists of two compact and planar detector heads with dimensions 16.5/spl times/14.5 cm/sup 2/ for breast and axilla imaging. Low-noise integrated electronics provide signal amplification and analog multiplexing based on a new data-driven architecture. The coincidence trigger and data acquisition architecture makes extensive use of pipeline processing structures and multi-event memories for high efficiency up to a data acquisition rate of one million events/s. Experimental validation of the detection techniques, namely the basic properties of the radiation sensors and the ability to measure the depth-of-interaction of the incoming photons, are presented. System performance in terms of detection sensitivity, count-rates and reconstructed image spatial resolution were also evaluated by means of a detailed Monte Carlo simulation and an iterative image reconstruction algorithm.

The amplitude of the signal collected from the PbWO4crystals of the CMS electromagnetic calorimeter is reconstructed by a digital filtering technique. The amplitude reconstruction has been studied...

In current CMOS nanometer technologies, aging effects may appear after relatively short operating times, compared to the expected lifetime of circuits. Therefore, there is an increasing need for on-chip aging monitoring, especially in high-performance, safety critical systems. This paper presents a programmable aging sensor that can be embedded in field-programmable gate array (FPGA)-based designs, using standard resources available in those devices and with minimal impact on the standard FPGA design flow. Given the limited amount of resources required by the sensor, it can be instantiated not only in the critical paths of a circuit, but also in those that may be identified to be more likely affected by aging effects. Experimental results, obtained in circuits of increasing complexity (where several sensors need to be used), are presented and discussed, demonstrating the good performance of the proposed sensor, as well as its low cost in terms of area overhead and power consumption. Results of aging experiments based on the standard US MIL-STD-883 Method 1015.50 “Burn-In Test” are also reported, demonstrating that the effect of aging on the sensor is negligible compared to that on the circuit under test, which is a key point for practical applicability. The proposed approach provides a novel and efficient solution to the specific FPGA design problems in this context, which are different from those addressed in application-specific integrated circuit design.

This paper presents a new approach on aging sensors for synchronous digital circuits. An adaptive error-prediction flip-flop architecture with built-in aging sensor is proposed, performing on-line monitoring of long-term performance degradation of CMOS digital systems. The main advantage is that the sensor's performance degradation works in favor of the predictive error detection. The sensor is out of the signal path. Performance error prediction is implemented by the detection of late transitions at flip-flop data input, caused by aging (namely, due to NBTI), or to physical defects activated by long lifetime operation. Such errors must not occur in safety-critical systems (automotive, health, space). A sensor insertion algorithm is also proposed, to selectively insert them in key locations in the design. Sensors can be always active or at pre-defined states. Simulation results are presented for a balanced pipeline multiplier in 65 nm CMOS technology, using Berkeley Predictive Technology Models (PTM). It is shown that the impact of aging degradation and/or PVT (Process, power supply Voltage and Temperature) variations on the sensor enhance error prediction.