The inherent simplicity of the LMS (Least Mean Square) Algorithm has lead to its wide usage. However, it is well known that high speed convergence and low final misadjustment cannot be realized simultaneously by the conventional LMS method. To overcome this trade-off problem, a new adaptive algorithm using Multiple ADF's (Adaptive Digital Filters) is proposed. The proposed algorithm modifies coefficients using multiple gradient vectors of the squared error, which are computed at different points on the performance surface. First, the proposed algorithm using 2 ADF's is discussed. Simulation results show that both high speed convergence and low final misadjustment can be realized. The computation time of this proposed algorithm is nearly as much as that of LMS if parallel processing techniques are used. Moreover, the proposed algorithm using more than 2 ADF's is discussed. It is understood that if more than 2 ADF's are used, further improvement in the convergence speed in not realized, but a reduction of the final misadjustment and an improvement in the stability are realized. Finally, a method which can improve the convergence property in the presence of correlated input is discussed. It is indicated that using priori knowledge and matrix transformation, the convergence property is quite improved even when a strongly correlated signal input is applied.

High-resolution bio-imaging is a key component for the advancement of life science. CMOS electronics is one of the promising candidates for emerging high-resolution devices because it offers nano-scale transistors. However, the resolution of the existing CMOS bio-imaging devices is several micrometers, which is insufficient for analyzing small objects such as bacteria and viruses. This paper presents the results of an analysis of the scalability of a current-mode analog-to-time converter (CMATC) to develop a high-resolution CMOS biosensor array. Simulations were performed using 0.6-µm, 0.25-µm, and 0.065-µm CMOS technology nodes. The Simulation results for the power consumption and resolution (cell size) showed that the CMATC has high-scalability and is a promising candidate to enable high-resolution CMOS bio-imaging.