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
_para
using the NN was effective and powerful, reducing process cost and feedback time.