For the purpose of realizing a new intelligent system and its simplified VLSI implementation, we propose a new nonvolatile analog memory called "switched diffusion analog memory with feedback circuit (FBSDAM). " FBSDAM has linear writing and erasing characteristics. Therefore, FBSDAM is useful for memorizing an analog value exactly. We also propose a new analog content-addressable memory (CAM) which has neural-like learning and discriminating functions which discriminate whether an incoming pattern is an unknown pattern or a stored pattern. We design and fabricate the CAM using FBSDAM by means of the 4µm double-poly single-metal CMOS process and nonvolatile analog memory technology which are developed by us. The chip size is 3.1 mm3.1 mm. We estimate that the CAM is composed of 50 times fewer transistors and requires 70 times fewer calculation steps than a typical digital computer implemented using similar technology.

We have fabricated a new analog memory with a floating gate as a key component to store synaptic weights for integrated artificial neural networks. The new analog memory comprises a tunnel junction (poly-Si/poly-si oxide/poly-Si sandwich structure), a thin-film transistor, two capacitors, and a floating gate MOSFET. The diffusion of the charges injected through the tunnel junction is controlled by switching operation of the thin-film transistor, and we refer to the new analog memory as switched diffusion analog memory (SDAM). The obtained characteristics of SDAM are a fast switching speed and an improved linearity between the potential of the floating gate and the number of pulse inputs. SDAM can be used in a neural network in which write/erase and read operations are performed simultaneously.