Entropy is a measure of uncertainty or randomness. It is the foundation for almost all cryptographic systems. True random number generators (TRNGs) and physical unclonable functions (PUFs) are the silicon primitives to respectively harvest dynamic and static entropy to generate random bit streams. In this survey paper, we present a systematic and comprehensive review of different state-of-the-art methods to harvest entropy from silicon-based devices, including the implementations, applications, and the security of the designs. Furthermore, we conclude the trends of the entropy source design to point out the current spots of entropy harvesting.

A novel low complexity TDC/ADC hybrid reconstruction readout circuit (ROIC) is proposed for LiDAR. Compared with other TDC-based receivers, the proposed circuit can provide a higher sampling speed, while consuming less power than ADC-based receivers. The circuit structure is constructed based on a sampler circuit to realize full waveform reconstruction. To further reduce power consumption, a Time Control Technique (TCT) is utilized to enable the sampler circuit to work only when needed. More specifically, the bandwidth, gain and input referred noise current spectral density of analog front-end (AFE) circuit are set as 150 M, 83 dB and 3.25 pA/sqrt (Hz), respectively. The experiment results demonstrate the feasibility that the sampler circuit can reach more than 3 GHz sampling frequency with only 2.8 mW power consumption.

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In tape placement process, the laying angle and laying sequence of laminates have proven their significant effects on the mechanical properties of carbon fibre reinforced composite material, specifically, laminates. In order to optimise these process parameters, an optimisation algorithm is developed based on the principles of genetic algorithms for improving the precision of traditional genetic algorithms and resolving the premature phenomenon in the optimisation process. Taking multi-layer symmetrically laid carbon fibre laminates as the research object, this algorithm adopts binary coding to conduct the optimisation of process parameters and mechanical analysis with the laying angle as the design variable and the strength ratio R as the response variable. A case study was conducted and its results were validated by the finite element analyses. The results show that the stresses before and after optimisation are 116.0 MPa and 100.9 MPa, respectively, with a decrease of strength ratio by 13.02%. The results comparison indicates that, in the iterative process, the search range is reduced by determining the code and location of important genes, thereby reducing the computational workload by 21.03% in terms of time consumed. Through multiple calculations, it validates that “gene mutation” is an indispensable part of the genetic algorithm in the iterative process.

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