Ionizing radiation effect and failure mechanism of Digital signal processor(DSP) is studied through test-board and automatic test equipment to find the relationship between system function failure and parameter degrad...
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Ionizing radiation effect and failure mechanism of Digital signal processor(DSP) is studied through test-board and automatic test equipment to find the relationship between system function failure and parameter degradation. Static bias is more sensitive than dynamic bias when DSP is tested on-line during radiation. Core current, high-Z leakage current and timing parameter are sensitive to ionizing radiation. No enhanced low-dose-rate sensitivity is found by comparing experiment results under high and low dose rate *** memory interface and Timer are deduced to be the sensitive module by step radiation and analysis basing full parameter test in Verigy 93000. The timing parameter degradation have a strong correlation to these module functions. And the degeneration mechanism is analysed on inverter through Hspice simulation which indicate the leakage circuit caused by radiation can lead a delay to the digital signal propagating. The parasitical capacitance among long connections make it worse to the data transmission around DSP, field programmable gate array and memory. Then an early function failure occurs in test board than Verigy 93000. This work provide support to systematic radiation hardness design and hardness assurance/lot acceptance testing in space applications.
A module m function takes value 0 when the weighted sum of the input variables is congruent to 0 mod m and takes value 1 otherwise. In this work we show a polynomial time algorithm for learning with equivalence querie...
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A module m function takes value 0 when the weighted sum of the input variables is congruent to 0 mod m and takes value 1 otherwise. In this work we show a polynomial time algorithm for learning with equivalence queries the class of conjunctions of module p functions on n variables, for any fixed prime p. The result is also extended to learning with bounded adversarial noise.
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