mpw-007 / NSDM N+ Implant

the NSDM shapes of all of mpw-007

40 of 40 dies rendered.

45,913,148 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (67.7 MB in total, largest 3.4 MB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-007_NSDM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-007_NSDM_reticle.png
the stitched reticle, full resolution
57.8 MB a6d246709791ffe88e52003a03eb797fcf9df94121d87fdc43a7fdbfdfbca87f8b11dbf1 62251ece71716d289d19a897175c0ecd590b611a192198a7ffa08313529171bc
mpw-007_NSDM_reticle_overview.png
the same image scaled to fit 4096 px
2.6 MB bac8c73e627028853e148bc3cb8642c04110b92366d231b7174e54c72fc30cc8d0842ce5 859802efa4c11d2a9fb7463f9bec85e902f484e6a92116854eee06d252a6db22
mpw-007_NSDM_tiles.json
where every tile belongs, and its checksum
13.6 kB 7212808ada19488551df6d2e9d7514a6b3a69efefa61f92e390966a6cc728936ae563113 2adcc3bb45939f3e52b00c8da0b2d88ad5e6ff560762c640a31e257c289784a2
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 tinytapeout 7576×107760, 0 1.3 MBa9c009065379615193c8f4082b4f70c68ef047e013d69ade360ad81cfa477394
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 1.7 MBc534da2f21d4adfe05534e446907eb14205de21f4ec7c1f5d4f50ef1afecca0d
A3 microwatt_mpw7 7576×1077615152, 0 2.5 MB02090b48f6cdc6569d4eaf48d0ec8ba3322b9eb5296e5979b6bb9f52dc0f0069
A4 yonga-can_controller 7576×1077622728, 0 1.1 MB4243d859f0c5ed0e86b937ff5eb06e2038ad16011e5c1ae509bd398643074876
A5 rocketalpha 7576×1077630304, 0 2.7 MB7117d10c4c0f451f996dd47adc9b87c858e99b55953ceeed3e100df734d52d7c
A6 riscduino-dcore_d3_3 7576×1077637880, 0 2.8 MBb38913088b339ddb9b713b79f6da1e992cb5703c9c31224aafb5ae84505f328f
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 2.7 MB276fc165ad7d51afbfdea9ed742ecefa8ca727a9037bbae481a3c44826589fa5
B2 riscduino-score-s4-3 7576×107767576, 10776 2.5 MB7b4f9fc9099d5aa633b15a9129d9b418828e582b131df5849e9f214ca08b9fca
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 857.0 kBf5895a437644587a0e173bc081fee60ad7d45e6c3c3861b786d158edfe989a9d
B4 sramtestvehicleg3 7576×1077622728, 10776 1.0 MBa74152ce68f7417dde09226e0022f0d52554cd9259c9d7eead72711a2cba38e2
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 995.8 kB803c3894e6e077a47e7b26020a92d04be36fffe839d2c1f321aab172fb1081b0
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 1.2 MB9f4e6bc3eec6f101b3860170dff3577017b88631d81ef44937bdf3601850d61b
B7 reram_crossbar 7976×1077645456, 10776 1.2 MB9a3e8d50571d5cb9eb5395170680333453cd2b0636a9619017f13fae8cc1b5e3
C1 trainable-nn 7576×107760, 21552 2.1 MB316aee34c051dc22683ef8b7bae946539a83e5b73027bd48cc24322566ff7ef5
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 974.3 kB6c5084085bbe9118e2f52d0bba6f4f54a6e545657bced329cccae48840c1bf40
C3 bitcoin_mining_asics 7576×1077615152, 21552 1.1 MB998a5e2633d03b3ae3e75d8660ff9e7ed52557200435d140b6ec5f75620145c9
C4 yonga-mcu 7576×1077622728, 21552 2.0 MBdc4fbec468aeb3d5f9ec043abb156c403d6b2607143dc9f112743a4b6ece0956
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 3.4 MB0c600ad9ca188efec30dcabbe43ec639ade7db51140e1f936a2be8b2444e916f
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 2.7 MBbfbe0c67e0ca8503d5e115a545e96f3436dd83161555c532d723c256748fed1a
D1 mixed_signal_circuits-jun13 7576×107760, 32328 972.8 kB264f60d59a5388421d1f0d998bc7eb9e2f1648bf1a3c009a8f32ee12b9aecb55
D2 fpga_programming_management_unit 7576×107767576, 32328 2.0 MB9e548b44cdaa061db81e334602d7ddf11c2235c12c996eacfa2392cc3b27dcc2
D3 warp-v 7576×1077615152, 32328 1.3 MB04aadc1972266e9d2b2ef121724bcfd5afec1f9fa0fa0ef580c2c7707be3e242
D4 leaf__mpw7_ 7576×1077622728, 32328 1.5 MB181aa4076c0859ba7f1359cbece6461ee213ec4033b1e624b81b0e6f71bf6fcb
D5 in_memory_computing_sram 7576×1077630304, 32328 1.3 MBbc6ad90108cd987dd3d845954302ccf219058b6be606eaedbeb6ee299c727d69
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 1.1 MB0cb9ab311dbd89134d7bf1e98ab83865475a05ed0e07aa909aa0c53b3ee05036
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 1.0 MBc1edfa67fa5a85c8f810b14db8ed765e912fe3ddfcc050a89c15a9ab18bdbb9a
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 1.1 MB709e2bca127baba5f236ac95c5c270c222ca1fc49eb25890981589a0d3ec1674
E2 in_memory_computing_rram 7576×107767576, 43104 1.3 MB433cc99b98f33844bfd64ce3bbfd31c35e52945bd58f8476863acb38b4585c38
E3 rram_imc_v2_flat 7576×1077615152, 43104 1.3 MB433cc99b98f33844bfd64ce3bbfd31c35e52945bd58f8476863acb38b4585c38
E4 topmetalse-dps 7576×1077622728, 43104 1.0 MB62bf7f1e02d15e82820852aa62a2e093a273e48d744b3cb167c51553a93d4f9a
E5 chaos_automaton 7576×1077630304, 43104 3.1 MBf270d25e9dc4c2d89b486c726088a87b717e5e5a693f5d93743ee916fad2fafc
E6 hehecore_resubmission 7576×1077637880, 43104 2.1 MB122c2e9cee2985602fa1e56fa534f9d4f7f2371c0a6aa539a01a1c2aad3b1b11
E7 waveform_generator 7976×1077645456, 43104 1.3 MBabf07b14b3877af109b908f817ee26308d32a4224db73f9b971e7ae2e3a9e737
F1 rift2core 7576×111760, 53880 1.3 MBc2dff9694ca03d0b21929a17cdd1ca8df0105f2f8f1d09d204ebd9750728fd62
F2 rift2go_2300 7576×111767576, 53880 3.0 MBb461adf45197d242c6352fdf5f689e9e06d8ab840e92907df44cf8c6e35df801
F3 sloci_resubmission 7576×1117615152, 53880 992.2 kB1640b76db2081423dab5f6bc68b7d437abf306ff358c2e63b9fe98d9597bf223
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 1.1 MB0f8a1b9d5ec4b6a8a687f81c6e2a84066262c85473eb88b881e6e4afd011af20
F5 crypto_aes128 7576×1117630304, 53880 1.7 MB4b0324f41260b40e11906c230817bee871ebff4c4fcd1a0f475b94bade8baa20
F6 graphics_controller 7576×1117637880, 53880 1.6 MBbab17c43777a58b00dd9af678aec65fdf1bb46756de8a817a398f632e614ea66
F7 prga-test 7976×1117645456, 53880 2.5 MBb7c2e73e2d2f49dd4c5867fc4ccdf0c9aa95d56be54a3aa0a09599931b866296

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About NSDM on mpw-007

Resolved: reticle 5CS8016AC, run mpw-007, mask NSDM

Mask layer
NSDM — N+ Implant
Process step
85 — N+ source drain implant mask
Drawn layer
93:44
Layer on the mask-layer GDS
30:0
Used in SKY130
yes

mpw-007 — SKY130 MPW-7

Reticle set
5CS8016AC
Run date
2022-07-08
Submissions closed
2022-09-12
Projects
106
Identification
inferred — Sequence after 5CS8014AC=MPW-6 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-7 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-007/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.