The　self-mixing　interferometry　(SMI)　technique　is　an　emerging　sensing　technology　in　microscale　particle　classification.　However,　due　to　the　nature　of　the　SMI　effect　raised　by　a　microscattering　particle,　the　signal　analysis　suffers　from　many　problems　compared　with　a　macro　target,　such　as　lower　signal-to-noise　ratio　(SNR),　short　transit　time,　and　time-varying　modulation　strength.　Therefore,　the　particle　sizing　measurement　resolution　is　much　lower　than　the　one　in　typical　displace-ment　measurements.　To　solve　these　problems,　in　this　paper,　first,　a　theoretical　model　of　the　phase　variation　of　a　single　-particle　SMI　signal　burst　is　demonstrated　in　detail.　The　relationship　between　the　phase　variation　and　the　particle　size　is　investigated,　which　predicts　that　phase　observation　could　be　another　alternative　for　particle　detection.　Second,　combined　with　continuous　wavelet　transform　and　Hilbert　transform,　a　novel　phase-unwrapping　algorithm　is　proposed.　This　algorithm　can　implement　not　only　efficient　individual　burst　extraction　from　the　noisy　raw　signal,　but　also　precise　phase　calculation　for　particle　sizing.　The　measurement　shows　good　accuracy　over　a　range　from　100　nm　to　6　mu　m　with　our　algorithm,　proving　that　our　algorithm　enables　a　simple　and　reliable　quantitative　particle　characteristics　retrieval　and　analysis　methodology　for　microscale　particle　detection　in　biomedical　or　laser　manufacturing　fields.