Research on the knowledge recognition and modeling of machining feature geometric evolution

Efficient Parametric Frequency Conversions in Chalcogenide‐Loaded Etchless Thin‐Film Lithium Niobate Waveguides

“…[49,50] What's more, the conversion efficiency could be further improved by the introduction of high Qfactor microring resonators which are already accessible in the ChG photonic platform. [39,51] Besides, recently, Pao-Kang Chen et al have achieved an improved second-order nonlinear efficiency in nanophotonic lithium niobate waveguides by using an adapted poling approach to eliminate the impact of nanoscale inhomogeneity, [52] which also provides a good paradigm for the MPM strategy in our case.…”

“…Moreover, GeSbS has a wide transparent spectral range from 0.5 to 11 μm and negligible two-photon absorption (TPA). [39] These properties are desirable to form a low-loss waveguide structure that guides the involved waves far-separated in the spectrum. Furthermore, the manufacture of GeSbS waveguides is less challenging because it is compatible with established CMOS technology.…”

“…In addition, compared to the materials (e.g., TiO 2 , silicon nitride, and polymer) used in hybrid zero-𝜒 (2) material/LN photonic waveguides, the GeSbS material has broad transparency to enable low propagation loss from the visible to the mid-infrared region, large Kerr nonlinearity, considerable photoelastic coefficients, high damage threshold, and CMOS compatibility in fabrication. [39] ChG-based photonic devices are prone to access due to the good adhesion of ChG thin films to various substrates via simple single-source thermal evaporation and the mature techniques for producing ChG waveguides. [40][41][42] Different from the etching strategy of LN, the etching process of ChG involves both physical and chemical interactions, making it possible to obtain smooth and vertical waveguide sidewalls, which is critical for low propagation loss.…”

Automated process planning and cost estimation under material quality uncertainty

“…Tremendous effort has been put into developing an automated process planner as documented in a number of past studies. Those works evolved around feature recognition [12][13][14][15][16][17][18], knowledge representation [19][20][21][22][23][24][25] and inference engine [26,27], and integration of process planning, and upstream or downstream processes [28,29]. Some researchers applied different methods/technologies such as OPPS-PRI 2.0 system [30], genetic algorithms (GA) [31][32][33][34][35][36], imperialist competitive algorithm [37], energy-efficient oriented method [38], neural network-based system [39][40][41], fuzzy set theory/fuzzy logic method [39,42,43], agent-based methodology [44,45], Internet-based technology [46,47], functional blocks [48,49], Petri net model [50] and STEP-compliant method [51][52][53][54], just to name a few, f...…”