Matches in SemOpenAlex for { <https://semopenalex.org/work/W3118167224> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W3118167224 abstract "Artificial intelligence and machine learning have been widely used to replace human work. Analog integrated circuit design needs to adjust a large number of circuit parameters to satisfy the balance between various performance metrics, which now mostly rely on the experience of designers and sometimes of the intuitions. Machine learning has demonstrated the potential to aid the design of analog integrated circuits in the literature, whilst most of them adopted particle swarm intelligence and Bayesian optimization. However, the model training time and simulation run time are substantial when any circuit structure modification occurs. In this paper, Recurrent Neural Network (RNN) was used to automatically optimize the parameters sizing by giving requested performance. Training data sets for the RNN of component parameters and circuit performance were simulated using Cadence Spectre. After training for only 15 minutes, RNN learns to predict parameters by inputting gain, bandwidth, power and frequency, which can make critical circuit design decision significantly faster. The reliability and applicability of the algorithm was verified through the parameter prediction of integrated operational amplifier and VCO." @default.
- W3118167224 created "2021-01-05" @default.
- W3118167224 creator A5001568267 @default.
- W3118167224 creator A5027914313 @default.
- W3118167224 creator A5034865295 @default.
- W3118167224 creator A5055382560 @default.
- W3118167224 creator A5074200075 @default.
- W3118167224 date "2020-10-23" @default.
- W3118167224 modified "2023-10-16" @default.
- W3118167224 title "LSTM-RNN Based Analog IC Automated Sizing Model for Operational Amplifier and VCO" @default.
- W3118167224 cites W1566916904 @default.
- W3118167224 cites W1911540303 @default.
- W3118167224 cites W2064675550 @default.
- W3118167224 cites W2122410182 @default.
- W3118167224 cites W2804987245 @default.
- W3118167224 cites W2904529841 @default.
- W3118167224 cites W2919115771 @default.
- W3118167224 cites W2964121744 @default.
- W3118167224 cites W3036532492 @default.
- W3118167224 cites W3100083105 @default.
- W3118167224 doi "https://doi.org/10.1109/icicm50929.2020.9292252" @default.
- W3118167224 hasPublicationYear "2020" @default.
- W3118167224 type Work @default.
- W3118167224 sameAs 3118167224 @default.
- W3118167224 citedByCount "0" @default.
- W3118167224 crossrefType "proceedings-article" @default.
- W3118167224 hasAuthorship W3118167224A5001568267 @default.
- W3118167224 hasAuthorship W3118167224A5027914313 @default.
- W3118167224 hasAuthorship W3118167224A5034865295 @default.
- W3118167224 hasAuthorship W3118167224A5055382560 @default.
- W3118167224 hasAuthorship W3118167224A5074200075 @default.
- W3118167224 hasConcept C119599485 @default.
- W3118167224 hasConcept C119857082 @default.
- W3118167224 hasConcept C127413603 @default.
- W3118167224 hasConcept C134146338 @default.
- W3118167224 hasConcept C142362112 @default.
- W3118167224 hasConcept C145366948 @default.
- W3118167224 hasConcept C147168706 @default.
- W3118167224 hasConcept C153349607 @default.
- W3118167224 hasConcept C154945302 @default.
- W3118167224 hasConcept C194257627 @default.
- W3118167224 hasConcept C24326235 @default.
- W3118167224 hasConcept C2776257435 @default.
- W3118167224 hasConcept C2777767291 @default.
- W3118167224 hasConcept C2778049539 @default.
- W3118167224 hasConcept C29074008 @default.
- W3118167224 hasConcept C31258907 @default.
- W3118167224 hasConcept C41008148 @default.
- W3118167224 hasConcept C50644808 @default.
- W3118167224 hasConcept C85617194 @default.
- W3118167224 hasConceptScore W3118167224C119599485 @default.
- W3118167224 hasConceptScore W3118167224C119857082 @default.
- W3118167224 hasConceptScore W3118167224C127413603 @default.
- W3118167224 hasConceptScore W3118167224C134146338 @default.
- W3118167224 hasConceptScore W3118167224C142362112 @default.
- W3118167224 hasConceptScore W3118167224C145366948 @default.
- W3118167224 hasConceptScore W3118167224C147168706 @default.
- W3118167224 hasConceptScore W3118167224C153349607 @default.
- W3118167224 hasConceptScore W3118167224C154945302 @default.
- W3118167224 hasConceptScore W3118167224C194257627 @default.
- W3118167224 hasConceptScore W3118167224C24326235 @default.
- W3118167224 hasConceptScore W3118167224C2776257435 @default.
- W3118167224 hasConceptScore W3118167224C2777767291 @default.
- W3118167224 hasConceptScore W3118167224C2778049539 @default.
- W3118167224 hasConceptScore W3118167224C29074008 @default.
- W3118167224 hasConceptScore W3118167224C31258907 @default.
- W3118167224 hasConceptScore W3118167224C41008148 @default.
- W3118167224 hasConceptScore W3118167224C50644808 @default.
- W3118167224 hasConceptScore W3118167224C85617194 @default.
- W3118167224 hasLocation W31181672241 @default.
- W3118167224 hasOpenAccess W3118167224 @default.
- W3118167224 hasPrimaryLocation W31181672241 @default.
- W3118167224 hasRelatedWork W11883665 @default.
- W3118167224 hasRelatedWork W12034600 @default.
- W3118167224 hasRelatedWork W12236409 @default.
- W3118167224 hasRelatedWork W12508877 @default.
- W3118167224 hasRelatedWork W12582432 @default.
- W3118167224 hasRelatedWork W13678974 @default.
- W3118167224 hasRelatedWork W2683128 @default.
- W3118167224 hasRelatedWork W3279523 @default.
- W3118167224 hasRelatedWork W8688885 @default.
- W3118167224 hasRelatedWork W2925925 @default.
- W3118167224 isParatext "false" @default.
- W3118167224 isRetracted "false" @default.
- W3118167224 magId "3118167224" @default.
- W3118167224 workType "article" @default.