Extraction of Device Structural Parameters Through DC/AC Performance Using an MLP Neural Network Algorithm

We proposed a neural network (NN) approach that uses two multi-layer perceptron (MLP) NNs an encoder and a decoder to estimate the structural parameter (S para ) of a 14-nm node fully depleted silicon on insulator (FDSOI) field-effect transistor (FET). When outputs defined by the same input exist, the proposed NN algorithm achieves loss function convergence during NN training. The decoder takes inputs of on/off current ratio, delay, and power to represent DC/AC performance for high performance (HP), low operating power (LOP), and low standby power (LSTP) applications. With the pre-trained encoder learned with R coefficients of the regression plot over 0.99 and an average percent error of approximately 1%, the decoder was modeled to estimate the S para . Our decoder successfully estimated all S para within the range that satisfies the technology node. The tendency of S para satisfying the desired figure-of-merits (FOMs) in device design can be confirmed by comparing the estimated S para of the upper 5 % and 10 % cases. Furthermore, it can provide device design guidance from various perspectives by presenting numerous alternatives of distinct S para sets, even when the FOM value is the same (duplicate input values). If undesirable FOMs are extracted, it is possible to determine the causal S para and provide immediate process feedback on the related unit process using the S para estimated from the lower 5 % of FOMs. We performed a detailed physical analysis as an example of a delay in LOP application. NN estimation results were analyzed using gate length ( L_{g} ), SOI thickness ( T_{soi} ), and drain-side spacer length ( L_{spd} ), which mainly affect gate capacitance ( C_{g} ) and effective current ( I_{eff} ). In addition, source-side spacer length ( L_{sps} ) and source/drain junction gradient ( L_{sdj} ) showed behaviors different from those generally selected by human experts and cases where maximal values were not estimated within the set range. The estimation of S