Real-time transient error and induced noise cancellation in linear analog filters using learning-assisted adaptive analog checksums

Analog circuits are sensitive to signal aggressions and power supply noise, crosstalk coupling and alpha particle strikes can cause significant degradation of circuit´s SNR. This research proposes a novel approach to real-time transient error and induced noise cancellation in linear analog circuits using analog checksums. It is based on the use of state space representations of analog filters and is a significant advancement over prior research that addressed only hard parametric deviations. A key innovation is the use of less than minimum distance checksum codes for error detection and correction using real-time learning of the likely source of transient errors and noise within the analog circuit. By running a simple hardware-directed search algorithm, the circuit “learns” how best to compensate for the injected signal disturbances with low overhead under the assumption that the source of the injected errors/noise and the error/noise statistics are stationary over time. Successful simulations and preliminary experimental results demonstrate almost complete compensation of injected noise, therefore validating the proposal.